• Journal of Institute of Control, Robotics and Systems
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• v.14 no.9
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• pp.904-908
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• 2008

Every autonomous system like a robot needs a power source known as a battery. And proper management of the battery is very important for proper operation. To know State of Charge(SOC) of a battery is the very core of proper battery management. In this paper, the SOC estimation problem is tackled based on the well known Extended Kalman Filter(EKF). Combined the existing battery model is used and then EKF is employed to estimate the SOC. SOC table is constructed by extensive experiment under various conditions and used as a true SOC. To verify the estimation result, extensive experiment is performed with various loads. The comparison result shows the battery estimation problem can be well solved with the technique proposed in this paper. The result of this paper can be used to develop related autonomous system.

• Journal of Hydrogen and New Energy
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• v.30 no.2
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• pp.155-162
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• 2019

The energy management technology for an independent fuel cell-battery hybrid system is developed for a household usage. To develop an efficient energy management technology, a simulation model is first developed. After the model is verified with experimental results, three energy management schemes are developed. Three control techniques are a fuzzy logic control (FLC), a state machine control (SMC), and a hybrid method of FLC and SMC. As the fuel cell-battery hybrid system is used for a house, battery state of charge (SOC) regulation is the most important factor for an energy management because SOC should be kept constant every day for continuous usage. Three management schemes are compared to see SOC, power split, and fuel cell power variations effects. Experimental results are also presented and the most favorable strategy is the state machine combined fuzzy control method.

• Journal of Power Electronics
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• v.14 no.5
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• pp.1038-1046
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• 2014

Lithium-ion batteries are widely used in hybrid and pure electric vehicles. State-of-charge (SOC) estimation is a fundamental issue in vehicle power train control and battery management systems. This study proposes a novel model-based SOC estimation method that applies closed-loop state observer theory and a comprehensive battery model. The state-space model of lithium-ion battery is developed based on a three-order resistor-capacitor equivalent circuit model. The least square algorithm is used to identify model parameters. A multi-state closed-loop state observer is designed to predict the open-circuit voltage (OCV) of a battery based on the battery state-space model. Battery SOC can then be estimated based on the corresponding relationship between battery OCV and SOC. Finally, practical driving tests that use two types of typical driving cycle are performed to verify the proposed SOC estimation method. Test results prove that the proposed estimation method is reasonably accurate and exhibits accuracy in estimating SOC within 2% under different driving cycles.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.9
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• pp.96-102
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• 2015

This paper presents battery charging method using 3-phase AC-DC boost converter. General battery charging method is that charging the battery voltage to the reference voltage according to the constant current(CC) control, when it reaches the reference voltage, charging the battery fully according to the constant voltage(CV) control. However, battery chaging time is increased because of the battery impedance, constant current charging section which shoud take the large amount of charge is narrow, and constant voltage charging section which can generate insufficient charge is widen. To improve this problem, we proposes the method to reduce the charging time according to the SOC(State of Charge) estimation using battery impedance.

• Journal of Electrical Engineering and Technology
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• v.11 no.3
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• pp.629-638
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• 2016

This paper proposes a State-of-Charge (SOC) balancing control of Battery Power Modules (BPMs) for a modularized battery for Electric Vehicles (EVs) without additional balancing circuits. The BPMs are substituted with the single converter in EVs located between the battery and the inverter. The BPM is composed of a two-phase interleaved boost converter with battery modules. The discharge current of each battery module can be controlled individually by using the BPM to achieve a balanced state as well as increased utilization of the battery capacity. Also, an SOC balancing method is proposed to reduce the equalization time, which satisfies the regulation of a constant DC-link voltage and a demand of the output power. The proposed system and the SOC balancing method are verified through simulation and experiment.

• New & Renewable Energy
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• v.4 no.4
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• pp.65-71
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• 2008

Considering the world's environmental problem, HEVs are projected as one of the solution. The keys of the HEV cruise control are expanding the use of electric motor and operating the internal combustion engine in the efficient region. This paper presents a new structure of SOC sustaining model where state-machine control is used. The proposed model defines battery charging and discharging as states and SOC of the battery as control variables. In this paper, we introduce various methods in deterministic rule-based control for HEV and describe a new SOC sustaining controller used by state-machine.

• Journal of Power Electronics
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• v.17 no.6
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• pp.1625-1636
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• 2017

A power electronic transformer (PET) based on the cascaded H-bridge (CHB) and the isolated bidirectional DC/DC converter (IBDC) is capable of accommodating a large scale battery energy storage system (BESS) in the medium-voltage grid, and is referred to as a power electronic transformer based battery energy storage system (PET-BESS). This paper investigates the PET-BESS and proposes a coordinative control strategy for it. In the proposed method, the CHB controls the power flow and the battery state-of-charge (SOC) balancing, while the IBDC maintains the dc-link voltages with feedforward implementation of the power reference and the switch status of the CHB. State-feedback and linear quadratic Riccati (LQR) methods have been adopted in the CHB to control the grid current, active power and reactive power. A hybrid PWM modulating method is utilized to achieve SOC balancing, where battery SOC sorting is involved. The feedforward path of the power reference and the CHB switch status substantially reduces the dc-link voltage fluctuations under dynamic power variations. The effectiveness of the proposed control has been verified both by simulation and experimental results. The performance of the PET-BESS under bidirectional power flow has been improved, and the battery SOC values have been adjusted to converge.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.6
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• pp.122-128
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• 2011

This paper proposes a new control scheme of lithium ion battery units based on single phase multi-level converter. In the DC/AC converter applications using battery storage system, it is necessary to control the balancing voltage of individual battery units for high efficiency utilization. Using the proposed control scheme, the DC/AC single phase converter system is applied. To verify the effectiveness of the proposed control scheme, computer simulation is accomplished. In the computer simulation, lithium-ion battery units and single phase multi-level converter system are modeled and carried out using Psim simulation program. It will be helpful for design and applications of energy storage system with lithium-ion battery.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.601-611
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• 2017

This paper proposes a simultaneous control of frequency deviation and electric vehicles (EVs) battery state of charge (SOC) using load frequency control (LFC) and EV controllers. In order to provide both frequency stabilization and SOC schedule near optimal performance within the whole operating regions, a multiple-input multiple-output model predictive control (MIMO-MPC) is employed for the coordination of LFC and EV controllers. The MIMO-MPC is an effective model-based prediction which calculates future control signals by an optimization of quadratic programming based on the plant model, past manipulate, measured disturbance, and control signals. By optimizing the input and output weights of the MIMO-MPC using particle swarm optimization (PSO), the optimal MIMO-MPC for simultaneous control of the LFC and EVs, is able to stabilize the frequency fluctuation and maintain the desired battery SOC at the certain time, effectively. Simulation study in a two-area interconnected power system with wind farms shows the effectiveness of the proposed MIMO-MPC over the proportional integral (PI) controller and the decentralized vehicle to grid control (DVC) controller.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.4
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• pp.616-622
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• 2015

A microgrid which is composed of distributed generation systems, energy storage systems and loads is operated in the grid-connected mode and in the islanded mode. Especially, in the islanded mode, a microgrid should maintain frequency in the allowed range. The frequency is decided by a balance between power supply and power demand. In general, the frequency is controlled by using battery energy storage systems (BESSs) in the microgrid. Especially, droop control is applied to controlling BESSs in the microgrid. Meanwhile, over-charging and deep-discharging of BESS in operation and control cause life-shortening of batteries. In this paper, a fuzzy droop control is proposed to change droop gains adaptively by considering state of charge (SOC) of BESSs to improve the life cycle of the battery. The proposed fuzzy droop control adjusts droop gains based on SOC of BESSs in real time. In other to show the performance of the proposed fuzzy droop control, simulation based on Matlab/Simulink is performed. In addition, comparison of the convention droop control and the proposed fuzzy droop control is also performed.