scholarly journals A Deterioration Diagnosis Circuit of a Lithium-Ion Battery Using Microcomputer Implementing Z-Transform Convolution

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6868
Author(s):  
Naoyuki Yoshioka ◽  
Naoto Nagaoka
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Simple Algorithm ◽  
Current Method ◽  
Close Relation ◽  
Estimated Parameters ◽  
Battery Capacity ◽  
Equivalent Circuit Parameters ◽  
In Series ◽  
Parallel Circuit

The deterioration of lithium-ion batteries has been detected by an increase in the battery impedance by means of an alternating current method or a battery capacity test. These methods require an interruption of the operation because the battery has to be removed. A method estimating equivalent circuit parameters of a lithium-ion battery during operation is proposed in this article. The increase in the internal impedance has a close relation to the deterioration of the battery. The circuit is expressed by a resister and an RC parallel circuit connected in series. The parameters can be estimated by applying a convolution technique to the voltage and current fluctuation of the battery during operation. A diagnosis circuit using a microcomputer is developed using a simple algorithm employing z-transformation in the parameter estimation. The estimated parameters depend on its state of charge (SOC) and ambient temperature. The SOC dependency is solved by estimating timing, and the temperature dependency is corrected by a function derived in this article. The deterioration diagnosis of the battery can be applied to a solar power generation system, and the feasibility is discussed in this article.

An Active Equalizer for Serially Connected Lithium-Ion Battery Cells

2013 ◽  
Vol 732-733 ◽  
pp. 809-812 ◽  
Author(s):  
Hong Rui Liu ◽  
Chao Ying Xia
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
State Of Charge ◽  
Experimental Results ◽  
Battery Cell ◽  
Charging Current ◽  
In Series ◽  
Battery Cells

This paper proposes an equalizer for serially connected Lithium-ion battery cells. The battery cell with the lowest state of charge (SOC) is charged by the equalizer during the process of charging and discharging, and the balancing current is constant and controllable. Three unbalanced lithium-ion battery cells in series are selected as the experimental object by this paper. The discharging current under a certain UDDS and 20A charging current are used to complete respectively one time balancing experiment of discharging and charging to the three lithium-ion battery cells. The validity of the balancing strategy is confirmed in this paper according to the experimental results.

A generalized additive model-based data-driven solution for lithium-ion battery capacity prediction and local effects analysis

2021 ◽  
pp. 014233122110579
Author(s):  
Tao Chen ◽  
Ciwei Gao ◽  
Hongxun Hui ◽  
Qiushi Cui ◽  
Huan Long
Keyword(s):  
Energy Storage ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Additive Models ◽  
Data Driven ◽  
Local Effects ◽  
Interaction Terms ◽  
Battery Capacity ◽  
Formulation Parameters ◽  
Capacity Prediction

Lithium-ion battery-based energy storage systems have been widely utilized in many applications such as transportation electrification and smart grids. As a key health status indicator, battery performance would highly rely on its capacity, which is easily influenced by various electrode formulation parameters within a battery. Due to the strongly coupled electrical, chemical, thermal dynamics, predicting battery capacity, and analysing the local effects of interested parameters within battery is significantly important but challenging. This article proposes an effective data-driven method to achieve effective battery capacity prediction, as well as local effects analysis. The solution is derived by using generalized additive models (GAM) with different interaction terms. Comparison study illustrate that the proposed GAM-based solution is capable of not only performing satisfactory battery capacity predictions but also quantifying the local effects of five important battery electrode formulation parameters as well as their interaction terms. Due to data-driven nature and explainability, the proposed method could benefit battery capacity prediction in an efficient manner and facilitate battery control for many other energy storage system applications.

scholarly journals State-of-Health Estimate for the Lithium-Ion Battery Using Chi-Square and ELM-LSTM

2021 ◽  
Vol 12 (4) ◽  
pp. 228
Author(s):  
Jianfeng Jiang ◽  
Shaishai Zhao ◽  
Chaolong Zhang
Keyword(s):  
Neural Network ◽  
Lithium Ion Battery ◽  
Short Term Memory ◽  
Lithium Ion ◽  
Constant Current ◽  
State Of Health ◽  
Chi Square ◽  
Mean Temperature ◽  
Battery Capacity ◽  
Learning Machine

The state-of-health (SOH) estimation is of extreme importance for the performance maximization and upgrading of lithium-ion battery. This paper is concerned with neural-network-enabled battery SOH indication and estimation. The insight that motivates this work is that the chi-square of battery voltages of each constant current-constant voltage phrase and mean temperature could reflect the battery capacity loss effectively. An ensemble algorithm composed of extreme learning machine (ELM) and long short-term memory (LSTM) neural network is utilized to capture the underlying correspondence between the SOH, mean temperature and chi-square of battery voltages. NASA battery data and battery pack data are used to demonstrate the estimation procedures and performance of the proposed approach. The results show that the proposed approach can estimate the battery SOH accurately. Meanwhile, comparative experiments are designed to compare the proposed approach with the separate used method, and the proposed approach shows better estimation performance in the comparisons.

scholarly journals Charge and Discharge Behaviour of Li-Ion Batteries at Various Temperatures Containing LiCoO2 Nanostructured Cathode Produced by CCSO

2015 ◽  
Vol 15 (4) ◽  
pp. 301 ◽  
Author(s):  
Y.Y. Mamyrbayeva ◽  
R.E. Beissenov ◽  
M.A. Hobosyan ◽  
S.E. Kumekov ◽  
K.S. Martirosyan
Keyword(s):  
Lithium Ion Battery ◽  
Active Material ◽  
Lithium Ion ◽  
Synthetic Approach ◽  
Capacity Fade ◽  
Li Ion Batteries ◽  
Material Loss ◽  
Method Performance ◽  
Battery Capacity ◽  
Li Ion

<p>There are technical barriers for penetration market requesting rechargeable lithium-ion battery packs for portable devices that operate in extreme hot and cold environments. Many portable electronics are used in very cold (-40 °C) environments, and many medical devices need batteries that operate at high temperatures. Conventional Li-ion batteries start to suffer as the temperature drops below 0 °C and the internal impedance of the battery  increases. Battery capacity also reduced during the higher/lower temperatures. The present work describes the laboratory made lithium ion battery behaviour features at different operation temperatures. The pouch-type battery was prepared by exploiting LiCoO<sub>2</sub> cathode material synthesized by novel synthetic approach referred as Carbon Combustion Synthesis of Oxides (CCSO). The main goal of this paper focuses on evaluation of the efficiency of positive electrode produced by CCSO method. Performance studies of battery showed that the capacity fade of pouch type battery increases with increase in temperature. The experimental results demonstrate the dramatic effects on cell self-heating upon electrochemical performance. The study involves an extensive analysis of discharge and charge characteristics of battery at each temperature following 30 cycles. After 10 cycles, the battery cycled at RT and 45 °C showed, the capacity fade of 20% and 25% respectively. The discharge capacity for the battery cycled at 25 °C was found to be higher when compared with the battery cycled at 0 °C and 45 °C. The capacity of the battery also decreases when cycling at low temperatures. It was important time to charge the battery was only 2.5 hours to obtain identical nominal capacity under the charging protocol. The decrease capability of battery cycled at high temperature can be explained with secondary active material loss dominating the other losses.</p>

scholarly journals Machine Learning-Based Lithium-Ion Battery Capacity Estimation Exploiting Multi-Channel Charging Profiles

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 75143-75152 ◽  
Author(s):  
Yohwan Choi ◽  
Seunghyoung Ryu ◽  
Kyungnam Park ◽  
Hongseok Kim
Keyword(s):  
Machine Learning ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Capacity Estimation ◽  
Battery Capacity
Sign in / Sign up

Export Citation Format

Share Document