scholarly journals Lithium Iron Phosphate (LiFePO4) Battery Power System for Deepwater Emergency Operation

2017 ◽  
Vol 143 ◽  
pp. 348-353 ◽  
Author(s):  
W.D. Toh ◽  
B. Xu ◽  
J. Jia ◽  
C.S. Chin ◽  
J. Chiew ◽  
...  
Keyword(s):  
Power System ◽  
Lithium Iron Phosphate ◽  
Emergency Operation ◽  
Iron Phosphate ◽  
Battery Power ◽  
Lifepo4 Battery

scholarly journals State-of-Charge Estimation and Active Cell Pack Balancing Design of Lithium Battery Power System for Smart Electric Vehicle

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Z. C. Gao ◽  
C. S. Chin ◽  
W. D. Toh ◽  
J. Chiew ◽  
J. Jia
Keyword(s):  
Power System ◽  
Electric Vehicle ◽  
Lithium Iron Phosphate ◽  
Iron Phosphate ◽  
State Of Charge ◽  
Active Cell ◽  
Estimation Model ◽  
Battery Power ◽  
And Performance ◽  
Cell Balancing

This paper presents an integrated state-of-charge (SOC) estimation model and active cell balancing of a 12-cell lithium iron phosphate (LiFePO4) battery power system. The strong tracking cubature extended Kalman filter (STCEKF) gave an accurate SOC prediction compared to other Kalman-based filter algorithms. The proposed groupwise balancing of the multiple SOC exhibited a higher balancing speed and lower balancing loss than other cell balancing designs. The experimental results demonstrated the robustness and performance of the battery when subjected to current load profile of an electric vehicle under varying ambient temperature.

A One-class Classifier Based on a Hybrid Topology to Detect Faults in Power Cells

2021 ◽  
Author(s):  
Esteban Jove ◽  
José-Luis Casteleiro-Roca ◽  
Héctor Quintián ◽  
Francisco Zayas-Gato ◽  
Gianni Vercelli ◽  
...  
Keyword(s):  
Lithium Iron Phosphate ◽  
Real Data ◽  
Iron Phosphate ◽  
Hybrid Classifier ◽  
Hybrid Topology ◽  
General Terms ◽  
Real Value ◽  
One Class Classifier ◽  
Lifepo4 Battery ◽  
Operating Points

Abstract The use of batteries became essential in our daily life in electronic devices, electric vehicles and energy storage systems in general terms. As they play a key role in many devices, their design and implementation must follow a thorough test process to check their features at different operating points. In this circumstance, the appearance of any kind of deviation from the expected operation must be detected. This research deals with real data registered during the testing phase of a lithium iron phosphate—LiFePO4—battery. The process is divided into four different working points, alternating charging, discharging and resting periods. This work proposes a hybrid classifier, based on one-class techniques, whose aim is to detect anomalous situations during the battery test. The faults are created by modifying the measured cell temperature a slight ratio from their real value. A detailed analysis of each technique performance is presented. The average performance of the chosen classifier presents successful results.

scholarly journals Temperature Effect and Battery Charging Characteristics Analysis Based on Charging C-Rate

2019 ◽  
Vol 9 (1) ◽  
pp. 159-165 ◽  
Keyword(s):  
Lithium Iron Phosphate ◽  
Dynamic Performance ◽  
Lithium Ion ◽  
Iron Phosphate ◽  
Battery Life ◽  
Battery Charging ◽  
Charging Time ◽  
Fast Charging ◽  
Characteristics Analysis ◽  
Lifepo4 Battery

Developing fast charging proprieties for LiFePo4 battery is a key issue for a wider deployment of EV. The main drawback of LiFePo4 battery charging is overcharge, overcurrent and high temperature which affects longevity, efficiency, and battery life cycle. In this research, lithium iron phosphate (LiFePo4) battery is investigated for fast, and rapid charging with CC-CV principle. MATLAB/Simulink based custom-designed tool was developed. A dynamic model of lithium-ion phosphate battery is proposed in this research by considering the significant temperature and capacity fading effects. Results have shown that the LiFePo4 battery can be used for fast charging up to 100% and rapid charging up to 85% by maintaining the condition for lifespan of the battery and to shorten the charging time. The simulation results have been showed that, the constructed model can really represent the dynamic performance feature of the lithium-ion battery. The modified model can assess the efficiency of battery execution based on charging C-rate conditions.

scholarly journals Analysis of overcharging characteristics of a new type lithium iron phosphate battery for dc system of substation

2021 ◽  
Vol 248 ◽  
pp. 01066
Author(s):  
Liu Meijie ◽  
Gao Kai ◽  
Li Zhongwei ◽  
Qiu Peng ◽  
Meng Zhen
Keyword(s):  
Power System ◽  
Power Supply ◽  
Lithium Iron Phosphate ◽  
Rapid Development ◽  
Iron Phosphate ◽  
Operating Conditions ◽  
Lead Acid Battery ◽  
Dc Power ◽  
Emergency Power ◽  
Lead Acid

In recent years, the number of DC power system in substation has been increasing. And the technical transformation of DC power system, fault maintenance and other workload is also on the rise, therefore dc emergency power emerged. The lead-acid battery is usually adopted for traditional DC emergency power supply. The disadvantage of lead-acid battery in volume and quality makes it difficult to realize the portability and mobility of dc emergency power. Lithium iron phosphate battery technology is the frontier technology in the rapid development period. However, the characteristics are not studied clearly. This paper studies the characteristics of lithium iron phosphate battery in different ambient temperature, operating conditions, and current of charge and discharge, analyses and summarizes the characteristics of battery charge and discharge, so as to improve the maintenance of station DC power supply system and the reliability of power supply network.

scholarly journals Lithium Iron Phosphate Intelligent SOC Prediction for Efficient Electric Vehicle

2014 ◽  
Vol 875-877 ◽  
pp. 1613-1618
Author(s):  
Siti Fauziah Toha ◽  
Nur Hazima Faezaa ◽  
Nor Aziah Mohd. Azubair ◽  
Nizam Hanis ◽  
Mohd. Khair Hassan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Electric Vehicle ◽  
Lithium Iron Phosphate ◽  
Iron Phosphate ◽  
Driving Cycle ◽  
Varied Temperature ◽  
Modelling Techniques ◽  
Elman Recurrent Neural Network ◽  
Lifepo4 Battery

This paper presents modelling techniques for Lithium Iron Phosphate (LiFePO4) battery in an electric vehicle. Artificial intelligence techniques namely multi-layered perceptron neural network (MLPNN) and Elman recurrent neural network are devised to estimate the energy remained in the battery bank which referred to state of charge (SOC). The New European Driving Cycle (NEDC) test data is used to excite the cells in driving cycle-based conditions under varied temperature range [0-55]°C. Accurate SOC prediction is a key function for satisfactory implementation of Battery Supervisory System (BSS). It is demonstrated that artificial intelligence methods can be effectively used with highly accurate results. The accuracy of the modeling results is demonstrated through validation and correlation tests.

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