Thermoelectric High Power Generating Module Made by n-Ba8AlxSi46-x Clathrate

2008 ◽  
Vol 1102 ◽  
Author(s):  
Shinji Munetoh ◽  
Makoto Arita ◽  
Hideki Makiyama ◽  
Teruaki Motooka
Keyword(s):  
Figure Of Merit ◽  
Open Circuit Voltage ◽  
Thermoelectric Module ◽  
Lithium Ion ◽  
Open Circuit ◽  
Electric Resistance ◽  
Thermoelectric Figure ◽  
Arc Melting ◽  
In Series ◽  
Specific Electric Resistance

AbstractWe have developed a new thermoelectric power-generating module composed of 72 pieces of n-type Ba8Al18Si28 clathrate elements made by arc melting. The Seebeck coefficient, specific electric resistance and thermal conductivity of Ba8Al18Si28 clathrate were 250 μV/K, 1.9 mΩcm and 3.1 W/mK at 500 °C, respectively, and the thermoelectric figure of merit (ZT) was 0.8. The new thermoelectric module was constructed using only n-type thermoelectric elements connected in series with hook-shaped electrodes. The open-circuit voltage of the module increased with hot-side temperature up to 1.8 V at 500 °C and generated 0.24 W. The module was successfully used to charge lithium-ion batteries for mobile phones.

scholarly journals Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1054
Author(s):  
Kuo Yang ◽  
Yugui Tang ◽  
Zhen Zhang
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Soc Estimation ◽  
Battery Model

With the development of new energy vehicle technology, battery management systems used to monitor the state of the battery have been widely researched. The accuracy of the battery status assessment to a great extent depends on the accuracy of the battery model parameters. This paper proposes an improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries. Using a two-order equivalent circuit model, the battery model is divided into two parts based on fast dynamics and slow dynamics. The recursive least squares method is used to identify parameters of the battery, and then the SOC and the open-circuit voltage of the model is estimated with the extended Kalman filter. The two-module voltages are calculated using estimated open circuit voltage and initial parameters, and model parameters are constantly updated during iteration. The proposed method can be used to estimate the parameters and the SOC in real time, which does not need to know the state of SOC and the value of open circuit voltage in advance. The method is tested using data from dynamic stress tests, the root means squared error of the accuracy of the prediction model is about 0.01 V, and the average SOC estimation error is 0.0139. Results indicate that the method has higher accuracy in offline parameter identification and online state estimation than traditional recursive least squares methods.

scholarly journals Development of Life-cycle Detection and Extending Function for Reusing Automotive Lithium-ion Battery – Verified in 52V LiMnNiCoO2 Platform

2020 ◽  
Author(s):  
Wu-Yang Sean ◽  
Ana Pacheco
Keyword(s):  
Life Cycle ◽  
Lithium Ion Battery ◽  
Management System ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
Battery Pack ◽  
Battery Management System ◽  
Battery Management ◽  
Cycle Detection

Abstract For reusing automotive lithium-ion battery, an in-house battery management system is developed. To overcome the issues of life cycle and capacity of reused battery, an online function of estimating battery’s internal resistance and open-circuit voltage based on adaptive control theory are applied for monitoring life cycle and remained capacity of battery pack simultaneously. Furthermore, ultracapacitor is integrated in management system for sharing peak current to prolong life span of reused battery pack. The discharging ratio of ultracapacitor is adjusted manually under Pulse-Width-Modulation signal in battery management system. In case study in 52V LiMnNiCoO2 platform, results of estimated open-circuit voltage and internal resistances converge into stable values within 600(s). These two parameters provide precise estimation for electrical capacity and life cycle. It also shows constrained voltage drop both in the cases of 25% to 75% of ultracapacitors discharging ratio compared with single battery. Consequently, the Life-cycle detection and extending functions integrated in battery management system as a total solution for reused battery are established and verified.

scholarly journals An Adaptive Estimation Scheme for Open-Circuit Voltage of Power Lithium-Ion Battery

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Yun Zhang ◽  
Chenghui Zhang ◽  
Naxin Cui
Keyword(s):  
Lithium Ion Battery ◽  
Equivalent Circuit ◽  
Open Circuit Voltage ◽  
Adaptive Estimation ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Open Circuit ◽  
Adaptive Scheme ◽  
Power Battery ◽  
Estimation Scheme

Open-circuit voltage (OCV) is one of the most important parameters in determining state of charge (SoC) of power battery. The direct measurement of it is costly and time consuming. This paper describes an adaptive scheme that can be used to derive OCV of the power battery. The scheme only uses the measurable input (terminal current) and the measurable output (terminal voltage) signals of the battery system and is simple enough to enable online implement. Firstly an equivalent circuit model is employed to describe the polarization characteristic and the dynamic behavior of the lithium-ion battery; the state-space representation of the electrical performance for the battery is obtained based on the equivalent circuit model. Then the implementation procedure of the adaptive scheme is given; also the asymptotic convergence of the observer error and the boundedness of all the parameter estimates are proven. Finally, experiments are carried out, and the effectiveness of the adaptive estimation scheme is validated by the experimental results.

A Novel InSb Photodiode Infrared Sensor Operating at Room Temperature

2005 ◽  
Vol 891 ◽  
Author(s):  
Koichiro Ueno ◽  
Edson Gomes Camargo ◽  
Yoshifumi Kawakami ◽  
Yoshitaka Moriyasu ◽  
Kazuhiro Nagase ◽  
...  
Keyword(s):  
Room Temperature ◽  
Open Circuit Voltage ◽  
Open Circuit ◽  
Consumer Electronics ◽  
Electronic Equipment ◽  
Personal Computers ◽  
Infrared Sensor ◽  
Output Noise ◽  
In Series ◽  
Photovoltaic Mode

ABSTRACTA microchip-sized InSb photodiode based infrared sensor (InSb PDS) that operates at room temperature was developed. The InSb PDS consists of 700 photodiodes connected in series and consumes no power, because it works in photovoltaic mode to output an open-circuit voltage. The InSb PDS has a typical responsivity of 1,900 V/W and an output noise of 0.15 μV/Hz1/2. A detectivity of 2.8×108 cmHz1/2/W was obtained at 300 K. The InSb PDS has performance high enough for applications such as mobile electronic equipment, personal computers, and consumer electronics

Measurement and Prediction of Decomposed Energy Efficiencies of Lithium Ion Batteries With Two Charge Models

2021 ◽  
Vol 18 (3) ◽  
Author(s):  
Yuhao Huang ◽  
Yan Su ◽  
Akhil Garg
Keyword(s):  
Lithium Ion Batteries ◽  
Open Circuit Voltage ◽  
Prediction Method ◽  
Lithium Ion ◽  
Open Circuit ◽  
Constant Current ◽  
New Process ◽  
Artificial Neural Network Ann ◽  
Cv Model ◽  
Charge Discharge

Abstract A new process decomposed calculation method is developed to compare the cycle based charge, discharge, net, and overall energy efficiencies of lithium-ion batteries. Multi-cycle measurements for both constant current (CC) and constant current to constant voltage (CC-CV) charge models have been performed. Unlike most conventional efficiency calculation methods with one mean open-circuit voltage (OCV) curve, two OCV curves are calculated separately for the charge and discharge processes. These two OCV curves help to clarify the intra-cycle charge, discharge, net, and overall energy efficiencies. The relationships of efficiencies versus state of charge, state of quantity, and scaled stresses are demonstrated. Efficiency degradation patterns versus cycle numbers and scaled stresses are also illustrated with the artificial neural network (ANN) prediction method. The decaying ratios of the overall efficiencies are about 2% and 0.3% in the first 30 cycles, for CC and CC-CV, respectively. Hence, efficiencies of the CC-CV model are more stable compared with the CC model, which are shown by both experimental and ANN prediction results.

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