Seongjun Lee

A new direct current internal resistance and state of charge relationship for the Li-ion battery pulse power estimation

The conventional test to obtain the direct current internal resistance (DCIR) has only experimented with a duration time of 5 seconds in the discharge region[3]~[5]. To obtain the DCIR, the duration time, Deltat and the region condition are important for the hybrid electric vehicle (HEV). In this paper, a new measurement method to obtain a direct current internal resistance (DCIR) is proposed. The proposed approach is performed during 10 seconds in the charge and discharge regions in order to obtain the new relationship between the DCIR and the state of charge (SOC). Thus, this obtained data can be used to estimate the battery pulse power using the previous SOC algorithm, extended Kalman filter (EKF)[6], which includes the DCIR-SOC relationship. The experiments are achieved using a fresh 1.3 Ah 18650 type Li-ion battery at 25degC.

The State of Charge estimation employing empirical parameters measurements for various temperatures

The State of charge (SOC) estimation algorithm uses the model parameter, which includes capacity, open circuit voltage (OCV), resistance and capacitance. These model parameter values are subject to be changed at varying temperatures[1–4]. In order to estimate SOC for temperature variation, it is indispensable to include the temperature effect on batterys performance. In this paper, the state of charge estimation employing empirical parameters measurements is introduced for SOC estimation for various temperatures. The internal parameters are measured on Li-Ion batteries from 10°C through 50°C at an interval of 10°C. Especially, the modified parameters are applied to estimate SOC at below room temperature and low SOC. The measured results are incorporated in the extend kalman filter (EKF) algorithm[5,6] and verified by comparison of Ah-counting and EKF result. The estimation and the results are shown to be under 3%.

Discrimination of battery characteristics using discharging/charging voltage pattern recognition

It is very important to have methods to determine the battery performances, such as state of charge (SOC) and state of health (SOH). Therefore, there are several methods to determine the performances of a battery in these days. However, these methods have drawbacks — different electrochemical characteristics, serious parameter changes due to temperature and aging. After all, it is limited SOC and SOH estimation for good performances due to unexpected and inconsistent parameters. For verification, few experiments are implemented using Li-Ion batteries. In this paper, battery characteristics are discriminated using discharging/charging voltage (DCV) pattern. In general, the parameters variations are intimately linked with voltage information of a battery. Especially, two resistance — series resistance and diffusion resistance in the lumped parameter battery model affected terminal voltage with the pulse current. When the pulse current is commonly applied to the batteries, the magnitudes of increased or decreased voltages are different, however, the patterns of voltages are similar each other. So, these patterns are used to apply the discrimination of battery characteristics using the hamming network. This network demonstrated proper technique for using a neural network for pattern recognition. The purpose of this is to decide which representative DCV pattern is closest to the current DCV pattern. Through statistical analysis of measured voltages, the proposed method is developed for recognition a current DCV pattern as one of 10 representative DCV patterns. The direct current internal resistance (DCIR) results are used for verification. A total of 10 fresh 1.3Ah 18650 type Li-Ion batteries are used for DCV patterns and DCIR results at 25°C.

The determination of state of charge based on extended kalman filter using per-unit system and time constant principle

In order to cope with the parameter variations for aged battery, which result in erroneous SOC estimation, the SOC estimation algorithm of lithium-ion batteries based on extended kalman filter (EKF) using per-unit system and time constant principle is proposed. In this per-unit system, the absolute values of capacity, voltage and current for aged batteries are converted into relative ones and are applied to dynamic model of the EKF algorithm. Moreover, the parameter values (RDiff, C{suDiff) are applied to dynamic and measurement model of EKF algorithm by time constant principle. In comparison with Ah-counting and EKF results, the estimation results using the proposed EKF algorithm have error within ±3%. Few aged batteries (1.3Ah) for 18650 types are used for verification of the proposed method.

Discharging/Charging Voltage-Temperature Pattern Recognition for Improved SOC/Capacity Estimation and SOH Prediction at Various Temperatures

This study investigates an application of the Hamming network-dual extended Kalman filter (DEKF) based on pattern recognition for high accuracy state-of-charge (SOC)/capacity estimation and state-of-health (SOH) prediction at various temperatures. The averaged nine discharging/charging voltage-temperature (DCVT) patterns for ten fresh Li-Ion cells at experimental temperatures are measured as representative patterns, together with cell model parameters. Through statistical analysis, the Hamming network is applied to identify the representative pattern that matches most closely with the pattern of an arbitrary cell measured at any temperature. Based on temperature-checking process, model parameters for a representative DCVT pattern can then be applied to estimate SOC/capacity and to predict SOH of an arbitrary cell using the DEKF. This avoids the need for repeated parameter measuremet.

Regulated Peak Power Tracking (RPPT) System Using Parallel Converter Topologies

Regulated peak power tracking (RPPT) systems such as the series structure and the series-parallel structures are commonly used in satellite space power systems. However, these structures process the solar array power or the battery power to the load through two cascaded regulators during one orbit cycle, which reduces the energy transfer efficiency. Also the battery charging time is increased due to placement of converter between the battery and the solar array. In this paper a parallel structure has been proposed which can improve the energy transfer efficiency and the battery charging time for satellite space power RPPT systems. An analogue controller is used to control all of the required functions, such as load voltage regulation and solar array stabilization with maximum power point tracking (MPPT). In order to compare the system efficiency and the battery charging efficiency of the proposed structure with those of a series (conventional) structure and a simplified series-parallel structure, simulations are performed and the results are analyzed using a loss analysis model. The proposed structure charges the battery more quickly when compared to the other two structures. Also the efficiency of the proposed structure has been improved under different modes of solar array operation when compared with the other two structures. To verify the system, experiments are carried out under different modes of solar array operation, including PPT charge, battery discharge, and eclipse and trickle charge.

Practical Methodology of the Integrated Design and Power Control Unit for SHEV with Multiple Power Sources

Series hybrid electric vehicles (SHEVs) having multiple power sources such as an engine- generator (EnGen), a battery, and an ultra-capacitor require a power control unit with high power density and reliable control operation. However, manufacturing using separate individual power converters has the disadvantage of low power density and requires a large number of power and signal cable wires. It is also difficult to implement the optimal power distribution and fault management algorithm because of the communication delay between the units. In order to address these concerns, this approach presents a design methodology and a power control algorithm of an integrated power converter for the SHEVs powered by multiple power sources. In this work, the design methodology of the integrated power control unit (IPCU) is firstly elaborately described, and then efficient and reliable power distribution algorithms are proposed. The design works are verified with product-level and vehicle-level performance experiments on a 10-ton SHEV.