Zhanfeng Li

An integrated online adaptive state of charge estimation approach of high-power lithium-ion battery packs.

A novel online adaptive state of charge (SOC) estimation method is proposed, aiming to characterize the capacity state of all the connected cells in lithium-ion battery (LIB) packs. This method is realized using the extended Kalman filter (EKF) combined with Ampere-hour (Ah) integration and open circuit voltage (OCV) methods, in which the time-scale implementation is designed to reduce the computational cost and accommodate uncertain or time-varying parameters. The working principle of power LIBs and their basic characteristics are analysed by using the combined equivalent circuit model (ECM), which takes the discharging current rates and temperature as the core impacts, to realize the estimation. The original estimation value is initialized by using the Ah integral method, and then corrected by measuring the cell voltage to obtain the optimal estimation effect. Experiments under dynamic current conditions are performed to verify the accuracy and the real-time performance of this proposed method, the analysed result of which indicates that its good performance is in line with the estimation accuracy and real-time requirement of high-power LIB packs. The proposed multi-model SOC estimation method may be used in the real-time monitoring of the high-power LIB pack dynamic applications for working state measurement and control.

A Novel Online SOC Estimation Method for the Power Lithium Battery Pack Based on the Unscented Kalman Filter

The SOC (State Of Charge) estimation is a core aspect for the associated BMS (Battery Management System) equipment of the lithium battery pack, in which the KF (Kalman Filter) -based methods have been extensively used but suffers from the linearization accuracy drawbacks. A novel UKF (Unscented Kalman Filter) estimation method battery model is proposed for the SOC estimation of the lithium battery pack, in which the linearization treatment is not required and fewer Sigma data points are used, reducing the computational requirement of the SOC estimation. The UKF method improves the SOC covariance properties for the lithium battery pack, the estimation performance of which has been validated by the experimental results. The proposed SOC estimation method has a RMSE (Root Mean Square Error) value of 1.42%, playing an important role in the popularization and application of the lithium battery pack.

Laser Diode Drive Circuit Design for Carbon Monoxide TDLAS Measurement System

Online analysis of carbon monoxide with the high-precision, high-sensitivity is of great significance to control environment pollution and industrial production-line process. The influence of these factors, such as input voltage variation of load and ripple to driver, were analyzed. The hybrid-driven power, with functions of slow-start, constant power laser diode control and over-current protection, was designed by integrating the principles and operation requirement, and the damage mechanism of laser diode. The experimental tests reveal that the tuning performance and the stability of output wavelength are fully guaranteed, and that the designed laser diode drive circuit well meets the requirement of measurement based on TDLAS.

An Unattended Detection Method of Soil Respiration

An Unattended Detection method for monitoring the CO2 concentration in soil respiration is described. It is based on the gas pump and gas extraction with a dynamic air chamber assay, and the study of the relationship between data collected from on-line measurement of carbon dioxide and time, as well as the resistance to interfering. This method uses proposed utility line peak extraction method to extract the CO2 concentration in the soil respiration chamber, and sets up a mathematical model of soil CO2 concentration. The system equipment is independently developed and it is verified that the extraction method is feasible with the site automatically detection. This method which detects multiple signals at the same time and improves data reliability has a high reference value for the similar characteristics of data on-line extraction, comparing with the traditional CO2 concentrations of soil respiration detection methods.