Internal resistance is one of the important parameters in the Li-Ion battery. This paper identifies it using two different methods: electrochemical impedance spectroscopy (EIS) and parameter estimation based on equivalent circuit model (ECM). Comparing internal resistance, the conventional parameter estimation method yields a different value than EIS. Therefore, a hysteresis-free parameter identification method based on ECM is proposed. The proposed technique separates hysteresis resistance from the effective resistance. It precisely estimated actual internal resistance, which matches the internal resistance obtained from EIS. In addition, state of charge, open circuit voltage, and different internal equivalent circuit components were identified. The least square method was used to identify the parameters based on ECM. A parameter extraction algorithm to interpret impedance spectrum obtained from the EIS. The algorithm is based on the properties of Nyquist plot, phasor algebra, and resonances. Experiments were conducted using a cellphone pouch battery and a cylindrical 18650 battery.

In recent years, battery chargers are not only capable of charging a battery, but also can measure the internal impedance spectrum of a battery in real-time. Measured battery impedance represents electrochemical characteristics, which are used to analyze the internal conditions of a battery and can be represented by a circuit model. This paper presents an equivalent circuit parameter extraction algorithm based on the impedance spectrum measurement of a battery. The proposed algorithm is designed using the Nyquist theory and is applicable to lithium ion batteries specifically. The circuit simulation, ac perturbation, and parameter extraction are processed with the measured circuit parameters, which are obtained from electrochemical impedance spectroscopy (EIS) test of a 1000mAh Li-ion battery. It shows 0.0216mΩ difference for Ohmic resistance extraction based on the proposed simulation results and the selected parameters from experiment. The presented algorithm could be applicable to the battery charger, which is capable of obtaining on-line electrochemical impedance spectrum. The algorithm could be useful for a battery management system as well.

The thermal characteristics of a battery are vital in order to understand its health and safety. The thermal characteristics are influenced by charging techniques. This paper presents theoretical explanation, experimental validation, and an analytical method to quantify the heat loss influenced by charging techniques for a Li-Ion battery. Constant current constant voltage (CC-CV), sinusoidal ripple current constant voltage, and pulse current constant voltage charging profiles are used as test cases for validation. The electrical and entropic heat generation for different charging profiles are modeled and estimated based on battery equivalent parameters. The estimated heat generation is compared with the heat flux measurement from a differential scanning calorimeter. In addition to the heat flux measurement, temperature, state of charge, terminal voltage, and current were recorded and analyzed.

Multiple unit of PFC converters can be utilized for better power quality in the residential applications. It is important to set proper amount of reactive power contribution from each unit of PFC converter to reduce power loss and increase stability. To set the referenced amount of reactive power contribution for each PFC converter, a dynamic supervisory controller is necessary. In this paper, a wireless based supervisory controller is introduced to mitigate the problem. Multiple unit of unidirectional bridgeless ac-dc boost PFC converters were used as reactive power resources in a smart home application. The supervisory controller is comprised of a discrete Proportional-Integral controller and distributor which sets the reactive power references for each PFC converter. The controller was designed based on the systems identification method. The controller was designed and simulated considering different communication delays. In the ideal condition, the system had no communication delay. However, actual practical system had some reasonable delay. The effect of the use of supervisory controller was observed for dynamic load changing conditions. The simulation was done by using SIMULINK. Reactive power consumption from grid becomes 0VAR within 0.25 second for ideal condition. However, for the actual condition this become 9.35 seconds.

Conventional power converters control pulse width modulation (PWM) signals based on the directly sensed feedback signals or estimated state signals. Recently, information and communication technologies are beginning to integrate power converters into smart grid applications. This paper presents a Hardware-In-The-Loop Testbed using a WirelessHART network for smart home applications in a case study. The main components of the testbed are OP4510 RT-LAB-RCP/HIL systems, Texas Instruments Digital Signal Controllers and AwiaTech wireless modules. In this testbed, wireless based controller is integrated with conventional digital controller. OP4510 is used to simulate power stages of the power converters in real time. Texas Instruments Digital Signal Controllers provide PWM signals from the control loop and communication loop. AwiaTech wireless modules are used to build a WirelessHART network to communicate with power converter from a supervisory program. The case study is to support reactive power in smart home applications using 3 bridgeless unidirectional power factor correction (PFC) converters. For this scenario, supervisor collects reactive power measurement data from the input power node and wirelessly transfers reactive power command values to the home appliances driven by PFC converters. Thus, reactive power demand can be minimized. The experimental result demonstrates the performance of the testbed in different communication delays.