scholarly journals Thermal Model Parameter Identification of a Lithium Battery

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Dirk Nissing ◽  
Arindam Mahanta ◽  
Stefan van Sterkenburg
Keyword(s):  
Thermal Model ◽  
Lithium Battery ◽  
Least Square ◽  
Model Parameters ◽  
Battery Management ◽  
Single Measurement ◽  
Cell Temperature ◽  
Battery Cell ◽  
Battery Management Systems ◽  
Method Of Least Square

The temperature of a Lithium battery cell is important for its performance, efficiency, safety, and capacity and is influenced by the environmental temperature and by the charging and discharging process itself. Battery Management Systems (BMS) take into account this effect. As the temperature at the battery cell is difficult to measure, often the temperature is measured on or nearby the poles of the cell, although the accuracy of predicting the cell temperature with those quantities is limited. Therefore a thermal model of the battery is used in order to calculate and estimate the cell temperature. This paper uses a simple RC-network representation for the thermal model and shows how the thermal parameters are identified using input/output measurements only, where the load current of the battery represents the input while the temperatures at the poles represent the outputs of the measurement. With a single measurement the eight model parameters (thermal resistances, electric contact resistances, and heat capacities) can be determined using the method of least-square. Experimental results show that the simple model with the identified parameters fits very accurately to the measurements.

Dynamic Thermal Model for Cylindrical Battery Cell Temperature Modeling

2019 ◽  
Vol 95 (1) ◽  
pp. 371-377
Author(s):  
Marek Toman ◽  
Radoslav Cipin ◽  
Pavel Vorel ◽  
Dalibor Cervinka
Keyword(s):  
Thermal Model ◽  
Cell Temperature ◽  
Battery Cell ◽  
Temperature Modeling

scholarly journals A Data-Driven Framework to Predict Lithium-Ion Battery Cell Imbalance for Real-Time Battery Management Systems

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8492
Author(s):  
Chao Li ◽  
Assimina A. Pelegri
Keyword(s):  
Real Time ◽  
Lithium Ion ◽  
Training Data ◽  
Data Driven ◽  
Management Systems ◽  
Battery Management ◽  
Battery Cell ◽  
Operational Conditions ◽  
Battery Management Systems ◽  
Battery Cells

Models that can predict battery cells’ thermal and electrical behaviors are necessary for real-time battery management systems to regulate the imbalance within battery cells. This work introduces a Gaussian Process Regression (GPR)-based data-driven framework that succeeds the Multi-Scale Multi-Dimensional (MSMD) modeling structure. The framework can make highly accurate predictions at the same level as full-order full-distribution simulations based on MSMD. A pseudo-2D model is used to generate training data and is combined with a process that shifts computation burdens from real-time battery management systems to lab data preparation. The testing results highlight the reliability of the GPR-based data-driven framework in terms of accuracy and stability under various operational conditions.

scholarly journals Evaluation and Comparative Study of Cell Balancing Methods for Lithium-Ion Batteries Used in Electric Vehicles

2021 ◽  
Vol 10 (3) ◽  
pp. 471-479
Author(s):  
Thiruvonasundari Duraisamy ◽  
Kaliyaperumal Deepa
Keyword(s):  
Electric Vehicles ◽  
Lithium Ion ◽  
Least Square ◽  
Battery Life ◽  
Model Parameters ◽  
Battery Management System ◽  
Battery Management ◽  
Renewable Energy Resources ◽  
Voltage Deviation ◽  
Cell Balancing

Vehicle manufacturers positioned electric vehicles (EVs) and hybrid electric vehicles (HEVs) as reliable, safe and environmental friendly alternative to traditional fuel based vehicles. Charging EVs using renewable energy resources reduce greenhouse emissions. The Lithium-ion (Li-ion) batteries used in EVs are susceptible to failure due to voltage imbalance when connected to form a pack. Hence, it requires a proper balancing system categorised into passive and active systems based on the working principle. It is the prerogative of a battery management system (BMS) designer to choose an appropriate system depending on the application. This study compares and evaluates passive balancing system against widely used inductor based active balancing system in order to select an appropriate balancing scheme addressing battery efficiency and balancing speed for E-vehicle segment (E-bike, E-car and E-truck). The balancing systems are implemented using “top-balancing” algorithm which balance the cells voltages near the end of charge for better accuracy and effective balancing. The most important characteristics of the balancing systems such as degree of imbalance, power loss and temperature variation are determined by their influence on battery performance and cost. To enhance the battery life, Matlab-Simscape simulation-based analysis is performed in order to fine tune the cell balancing system for the optimal usage of the battery pack. For the simulation requirements, the battery model parameters are obtained using least-square fitting algorithm on the data obtained through electro chemical impedance spectroscopy (EIS) test. The achieved balancing time of the passive and active cell balancer for fourteen cells were 48 and 20 min for the voltage deviation of 30 mV. Also, the recorded balancing time was 215 and 42 min for the voltage deviation of 200 mV.

scholarly journals New Cell Balancing Charging System Research for Lithium-ion Batteries

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1393
Author(s):  
Chan-Yong Zun ◽  
Sang-Uk Park ◽  
Hyung-Soo Mok
Keyword(s):  
High Capacity ◽  
Lithium Ion ◽  
High Energy ◽  
High Energy Density ◽  
Battery Management ◽  
Battery Cell ◽  
Charging System ◽  
Charging Time ◽  
Battery Management Systems ◽  
Cell Balancing

With recent advancements in the electrical industry, the demand for high capacity and high energy density batteries has increased, subsequently increasing the demand for fast and reliable battery charging. A battery is an assembly of a plurality of cells, in which maintaining a balance between neighboring cells is crucial for stable charging. To this end, various methods have been applied to battery management systems. Representative methods for maintaining the balance in battery cells include a passive method of adjusting the balance using a resistor and an active method involving the exchange of energy between the cells. However, these methods are limited in terms of efficiency, lifespan, and charging time. Therefore, in this study, we propose a new charging method at the battery cell level and demonstrate its effectiveness through experiments.

scholarly journals Evaluation and Comparative Study of Cell Balancing Methods for Lithium-Ion Batteries Used in Electric Vehicles

2021 ◽  
Vol 10 (3) ◽  
pp. 471-479
Author(s):  
Thiruvonasundari Duraisamy ◽  
Kaliyaperumal Deepa
Keyword(s):  
Electric Vehicles ◽  
Lithium Ion ◽  
Least Square ◽  
Battery Life ◽  
Model Parameters ◽  
Battery Management System ◽  
Battery Management ◽  
Renewable Energy Resources ◽  
Voltage Deviation ◽  
Cell Balancing

Vehicle manufacturers positioned electric vehicles (EVs) and hybrid electric vehicles (HEVs) as reliable, safe and environmental friendly alternative to traditional fuel based vehicles. Charging EVs using renewable energy resources reduce greenhouse emissions. The Lithium-ion (Li-ion) batteries used in EVs are susceptible to failure due to voltage imbalance when connected to form a pack. Hence, it requires a proper balancing system categorised into passive and active systems based on the working principle. It is the prerogative of a battery management system (BMS) designer to choose an appropriate system depending on the application. This study compares and evaluates passive balancing system against widely used inductor based active balancing system in order to select an appropriate balancing scheme addressing battery efficiency and balancing speed for E-vehicle segment (E-bike, E-car and E-truck). The balancing systems are implemented using “top-balancing” algorithm which balance the cells voltages near the end of charge for better accuracy and effective balancing. The most important characteristics of the balancing systems such as degree of imbalance, power loss and temperature variation are determined by their influence on battery performance and cost. To enhance the battery life, Matlab-Simscape simulation-based analysis is performed in order to fine tune the cell balancing system for the optimal usage of the battery pack. For the simulation requirements, the battery model parameters are obtained using least-square fitting algorithm on the data obtained through electro chemical impedance spectroscopy (EIS) test. The achieved balancing time of the passive and active cell balancer for fourteen cells were 48 and 20 min for the voltage deviation of 30 mV. Also, the recorded balancing time was 215 and 42 min for the voltage deviation of 200 mV.

A Cell Balancing System Based on Charge Shuttling Method

2014 ◽  
Vol 986-987 ◽  
pp. 1892-1896
Author(s):  
Yu Wei Zhu ◽  
Bing Shi
Keyword(s):  
High Voltage ◽  
Management System ◽  
Lithium Battery ◽  
Low Voltage ◽  
Low Cost ◽  
Battery Management System ◽  
Battery Management ◽  
Battery Cell ◽  
A Cell ◽  
Cell Balancing

As one of the most essential part in a battery management system for a lithium battery, cell balancing determine its performance and lifetime. A new “flying capacitor” method is presented in this paper. Where, a clock-switched circuit changer matrix makes the charges flow from the high-voltage cells to the low-voltage cells. Some super-capacitors buffer the charge and redistribute energy of the cells in the battery. The implementation is also low-cost and its design period is short. The result shows that the method is feasible.

scholarly journals Lithium battery parameter identification and SOC estimation based on dual-polarized model

2021 ◽  
Vol 300 ◽  
pp. 01013
Author(s):  
Hui Xia ◽  
Changlei Li ◽  
Yusang Xu ◽  
Xuehong Liu
Keyword(s):  
Lithium Battery ◽  
Estimation Error ◽  
Equivalent Circuit Model ◽  
Least Square Method ◽  
Base Station ◽  
Least Square ◽  
Model Parameters ◽  
Recursive Least Square ◽  
Forgetting Factor ◽  
Recursive Least Square Method

An equivalent circuit model of dual polarization (DP) of lithium battery was established according to the application characteristics of lithium battery under the standby condition of 5G base station. On the basis of the model, recursive least square method with forgetting factor (RLS) was used to identify the model parameters. Finally, the Unscented Kalman filtering (UKF) was used to estimate the SOC of lithium battery in real time with the identified model parameters. The simulation and experimental results showed that the combined estimation using recursive least square method with forgetting factor (RLS) and UKF could greatly improve the estimation accuracy of lithium battery SOC, reduce the estimation error, and further verify the accuracy and effectiveness of the whole modeling.

Nonlinear Adaptive Observer for a Lithium-Ion Battery Cell Based on Coupled Electrochemical–Thermal Model

2015 ◽  
Vol 137 (11) ◽  
Author(s):  
S. Dey ◽  
B. Ayalew ◽  
P. Pisu
Keyword(s):  
Thermal Model ◽  
Adaptive Observer ◽  
Parameter Estimates ◽  
Physical Parameters ◽  
Model Parameters ◽  
Battery Cell ◽  
Thermal Dynamics ◽  
Adaptive Scheme ◽  
Li Ion ◽  
Battery Cells

Real-time estimation of battery internal states and physical parameters is of the utmost importance for intelligent battery management systems (BMS). Electrochemical models, derived from the principles of electrochemistry, are arguably more accurate in capturing the physical mechanism of the battery cells than their counterpart data-driven or equivalent circuit models (ECM). Moreover, the electrochemical phenomena inside the battery cells are coupled with the thermal dynamics of the cells. Therefore, consideration of the coupling between electrochemical and thermal dynamics inside the battery cell can be potentially advantageous for improving the accuracy of the estimation. In this paper, a nonlinear adaptive observer scheme is developed based on a coupled electrochemical–thermal model of a Li-ion battery cell. The proposed adaptive observer scheme estimates the distributed Li-ion concentration and temperature states inside the electrode, and some of the electrochemical model parameters, simultaneously. These states and parameters determine the state of charge (SOC) and state of health (SOH) of the battery cell. The adaptive scheme is split into two separate but coupled observers, which simplifies the design and gain tuning procedures. The design relies on a Lyapunov's stability analysis of the observers, which guarantees the convergence of the combined state-parameter estimates. To validate the effectiveness of the scheme, both simulation and experimental studies are performed. The results show that the adaptive scheme is able to estimate the desired variables with reasonable accuracy. Finally, some scenarios are described where the performance of the scheme degrades.

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