Weilin Luo

Study on impedance model of Li-ion battery

Impedance model contains a lot of information that can indicate performance of battery. Therefore, establishment of accurate impedance model is very important. In this paper, impedance model of Li-ion battery is examined by electrochemical mechanisms analysis and experimental measurement. The basis for the form of the model has been linked to internal processes of the battery and validated using experimental data. The impedance behavior during individual discharge cycles as well as over its cycle life is obtained. The proposed model will greatly help research on circuit simulation, multi-cell battery analysis, battery test and monitoring, prognostics and battery health management.

Parallelized Genetic Identification of the Thermal-Electrochemical Model for Lithium-Ion Battery

The parameters of a well predicted model can be used as health characteristics for Lithium-ion battery. This article reports a parallelized parameter identification of the thermal-electrochemical model, which significantly reduces the time consumption of parameter identification. Since the P2D model has the most predictability, it is chosen for further research and expanded to the thermal-electrochemical model by coupling thermal effect and temperature-dependent parameters. Then Genetic Algorithm is used for parameter identification, but it takes too much time because of the long time simulation of model. For this reason, a computer cluster is built by surplus computing resource in our laboratory based on Parallel Computing Toolbox and Distributed Computing Server in MATLAB. The performance of two parallelized methods, namely Single Program Multiple Data (SPMD) and parallel FOR loop (PARFOR), is investigated and then the parallelized GA identification is proposed. With this method, model simulations running parallelly and the parameter identification could be speeded up more than a dozen times, and the identification result is batter than that from serial GA. This conclusion is validated by model parameter identification of a real LiFePO4 battery.

A Method of Remaining Capacity Estimation for Lithium-Ion Battery

Combining particle filter (PF) with sample entropy feature of discharge voltage, a method of remaining capacity estimation for lithium-ion battery is proposed. The sample entropy calculated from discharge voltage curve can serve as an indicator for assessing the condition of battery. Under a certain working condition, a functional relationship between sample entropy and discharge capacity is created and estimations computed from the function are taken as observations to propagate particles in PF. The results indicate that the algorithm enhances the accuracy. Due to the establishment of functions at different discharge rates and temperature modification, prognostic accuracy of discharge capacity has been improved under multi-operating working conditions.

Research on the EFT radiation characteristic of classical structures used in Measuring-control System

This paper analyzed the Electrical Fast Transient (EFT) radiation interference characteristic and effect factors of three classical structures including: the electromagnetic relay, the inductance coil and the thin wire which are commonly used in the Measuring-control System. The EFT interference radiation model has been built by the NUFDTD method. This method divides the simulation space into a sort of grids which are non-uniform. Thus it can acquire a more detailed simulation model while using less computational resource. Use this method, the changing regularity of EFT radiation field, when the different stimulate source being put on the according structure, has been studied. At the same time, the corresponding relationship of the distributing characteristic of the EFT radiation field and the device structure has been discussed. Finally, based on the analysis and discuss conclusion, according to each classical structure, some anti-interference methods have been brought forward.

Identification of the Li + initial inserted rate of electrode materials in Li-ion batteries: Based on Multi-Objective Genetic Algorithm

In this article, Li+ initial inserted rate and stoichiometric window of electrode materials in Li-ion batteries are identified using Multi-Objective Genetic Algorithm (MOGA). The article is motivated by the problem of fitting both the OCP model and incremental capacity analysis (ICA) curve, which comes from OCP curve and has higher parameter sensitivity, of batteries to those experimental data. A dynamic weight coefficient is also proposed to deal with the multi-objective problem by transforming the MOGA to GA. LiCoO2 and LiFePO4 systems are investigate.