High accuracy temperaure-dependent SOC estimation based on real-time parameter identification for rechargeable Li-Ion battery pack

2019 ◽  
Author(s):  
Jinhyeong Park ◽  
Hynsu Bae ◽  
Sung-Soo Jang ◽  
Woonki Na ◽  
Jonghoon Kim
Keyword(s):  
Parameter Identification ◽  
Real Time ◽  
High Accuracy ◽  
Time Parameter ◽  
Battery Pack ◽  
Li Ion Battery ◽  
Soc Estimation ◽  
Li Ion

Real Time Prediction of Li-Ion Battery Pack Temperatures in EV Vehicles

2020 ◽  
Author(s):  
Azita Soleymani ◽  
William Maltz
Keyword(s):  
Real Time ◽  
Heat Generation ◽  
Equivalent Circuit Model ◽  
Battery Pack ◽  
Generation Rate ◽  
Li Ion Battery ◽  
Heat Generation Rate ◽  
Digital Twin ◽  
Twin Model ◽  
Li Ion

Abstract A semi-analytical digital twin model of a 90 kW.h li-ion battery pack was developed to capture thermal behavior of the pack in a real-time environment. The solution uses reduced-order models that minimize compute cost/time yet are accurate in predicting real-world operation. The real-time heat generation rate in the battery pack is calculated using 2RC equivalent circuit model. A series of HPPC tests were conducted to calibrate the equivalent circuit model in order to accurately calculate heat generation rate as a function of SOC, temperature, current, charge/discharge mode and pulse duration. In the paper, live-sensor data was integrated into the digital twin system level model of the battery pack to create a real-time environment. The generated tool was utilized to monitor the real-time temperature of the battery pack remotely and have a predictive maintenance solution. The model results for heat generation rate, terminal voltage, and temperature were found to be consistent with the test data across a wide range of conditions. The generated model was used to accelerate battery pack design and development by enabling the evaluation of design feasibility and to conduct in-depth root causes analyses for various inputs and operating conditions, including initial SOC, temperature, coolant flow rate, different charge and discharge profiles. The resulting digital twin model provides additional data that cannot be measured offering the EV industry an opportunity to improve its safety record.

Development of a Two-Dimensional Thermal Model for Li-Ion Battery Pack With Experimental Validation

2019 ◽  
Vol 12 (2) ◽  
Author(s):  
Haoting Wang ◽  
Ning Liu ◽  
Lin Ma
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Experimental Validation ◽  
Computational Cost ◽  
Battery Pack ◽  
Two Dimensional ◽  
Li Ion Battery ◽  
New Approach ◽  
Battery Packs ◽  
Li Ion

Abstract This paper reports the development of a two-dimensional two states (2D2S) model for the analysis of thermal behaviors of Li-ion battery packs and its experimental validation. This development was motivated by the need to fill a niche in our current modeling capabilities: the need to analyze 2D temperature (T) distributions in large-scale battery packs in real time. Past models were predominately developed to either provide detailed T information with high computational cost or provide real-time analysis but only 1D lumped T information. However, the capability to model 2D T field in real time is desirable in many applications ranging from the optimal design of cooling strategies to onboard monitoring and control. Therefore, this work developed a new approach to provide this desired capability. The key innovations in our new approach involved modeling the whole battery pack as a complete thermal-fluid network and at the same time calculating only two states (surface and core T) for each cell. Modeling the whole pack as a complete network captured the interactions between cells and enabled the accurate resolution of the 2D T distribution. Limiting the calculation to only the surface and core T controlled the computational cost at a manageable level and rendered the model suitable for packs at large scale with many cells.

scholarly journals Li-Ion Battery Pack SoC Estimation for Electric Vehicles

2018 ◽  
Author(s):  
Kodjo Senou Rodolphe Mawonou ◽  
Akram Eddahech ◽  
Didier Dumur ◽  
Emmanuel Godoy ◽  
Dominique Beauvois ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Battery Pack ◽  
Li Ion Battery ◽  
Soc Estimation ◽  
Li Ion

scholarly journals Real Time Design and Implementation of State of Charge Estimators for a Rechargeable Lithium-Ion Cobalt Battery with Applicability in HEVs/EVs—A Comparative Study

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2749 ◽  
Author(s):  
Nicolae Tudoroiu ◽  
Mohammed Zaheeruddin ◽  
Roxana-Elena Tudoroiu
Keyword(s):  
Comparative Study ◽  
Real Time ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Li Ion Battery ◽  
Matlab Simulation ◽  
Soc Estimation ◽  
Battery Model ◽  
Li Ion

Estimating the state of charge (SOC) of Li-ion batteries is an essential task of battery management systems for hybrid and electric vehicles. Encouraged by some preliminary results from the control systems field, the goal of this work is to design and implement in a friendly real-time MATLAB simulation environment two Li-ion battery SOC estimators, using as a case study a rechargeable battery of 5.4 Ah cobalt lithium-ion type. The choice of cobalt Li-ion battery model is motivated by its promising potential for future developments in the HEV/EVs applications. The model validation is performed using the software package ADVISOR 3.2, widely spread in the automotive industry. Rigorous performance analysis of both SOC estimators is done in terms of speed convergence, estimation accuracy and robustness, based on the MATLAB simulation results. The particularity of this research work is given by the results of its comprehensive and exciting comparative study that successfully achieves all the goals proposed by the research objectives. In this scientific research study, a practical MATLAB/Simscape battery model is adopted and validated based on the results obtained from three different driving cycles tests and is in accordance with the required specifications. In the new modelling version, it is a simple and accurate model, easy to implement in real-time and offers beneficial support for the design and MATLAB implementation of both SOC estimators. Also, the adaptive extended Kalman filter SOC estimation performance is excellent and comparable to those presented in the state-of-the-art SOC estimation methods analysis.

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