Real Time Prediction of Temperature and Heat Generation Rate of Li-Ion Battery Packs in Ev Vehicles

2021 ◽  
Author(s):  
Azita Soleymani ◽  
William Maltz
Keyword(s):  
Real Time ◽  
Heat Generation ◽  
Generation Rate ◽  
Li Ion Battery ◽  
Heat Generation Rate ◽  
Time Prediction ◽  
Battery Packs ◽  
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.

Heat generation rate measurement in a Li-ion cell at large C-rates through temperature and heat flux measurements

2015 ◽  
Vol 285 ◽  
pp. 266-273 ◽  
Author(s):  
S.J. Drake ◽  
M. Martin ◽  
D.A. Wetz ◽  
J.K. Ostanek ◽  
S.P. Miller ◽  
...  
Keyword(s):  
Heat Flux ◽  
Heat Generation ◽  
Generation Rate ◽  
Rate Measurement ◽  
Heat Generation Rate ◽  
Flux Measurements ◽  
Li Ion

scholarly journals A Review of State of Health Estimation of Energy Storage Systems: Challenges and Possible Solutions for Futuristic Applications of Li-Ion Battery Packs in Electric Vehicles

2019 ◽  
Vol 16 (4) ◽  
Author(s):  
Sudipta Bijoy Sarmah ◽  
Pankaj Kalita ◽  
Akhil Garg ◽  
Xiao-dong Niu ◽  
Xing-Wei Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Time Estimation ◽  
State Of Health ◽  
Li Ion Battery ◽  
Super Capacitor ◽  
Advantages And Disadvantages ◽  
Battery Packs ◽  
Li Ion

Lithium-ion (Li-ion) battery pack is vital for storage of energy produced from different sources and has been extensively used for various applications such as electric vehicles (EVs), watches, cookers, etc. For an efficient real-time monitoring and fault diagnosis of battery operated systems, it is important to have a quantified information on the state-of-health (SoH) of batteries. This paper conducts comprehensive literature studies on advancement, challenges, concerns, and futuristic aspects of models and methods for SoH estimation of batteries. Based on the studies, the methods and models for SoH estimation have been summarized systematically with their advantages and disadvantages in tabular format. The prime emphasis of this review was attributed toward the development of a hybridized method which computes SoH of batteries accurately in real-time and takes self-discharge into its account. At the end, the summary of research findings and the future directions of research such as nondestructive tests (NDT) for real-time estimation of battery SoH, finding residual SoH for the recycled batteries from battery packs, integration of mechanical aspects of battery with temperature, easy assembling–dissembling of battery packs, and hybridization of battery packs with photovoltaic and super capacitor are discussed.

Energy efficiency of Li-ion battery packs re-used in stationary power applications

2014 ◽  
Vol 8 ◽  
pp. 9-17 ◽  
Author(s):  
Leila Ahmadi ◽  
Michael Fowler ◽  
Steven B. Young ◽  
Roydon A. Fraser ◽  
Ben Gaffney ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Li Ion Battery ◽  
Battery Packs ◽  
Li Ion

Real-Time Estimation of Lithium-Ion Concentration in Both Electrodes of a Lithium-Ion Battery Cell Utilizing Electrochemical–Thermal Coupling

2017 ◽  
Vol 139 (3) ◽  
Author(s):  
Satadru Dey ◽  
Beshah Ayalew
Keyword(s):  
Real Time ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
Time Estimation ◽  
Li Ion Battery ◽  
Battery Cell ◽  
Thermal Dynamics ◽  
Estimation Scheme ◽  
Real Time Estimation ◽  
Li Ion

This paper proposes and demonstrates an estimation scheme for Li-ion concentrations in both electrodes of a Li-ion battery cell. The well-known observability deficiencies in the two-electrode electrochemical models of Li-ion battery cells are first overcome by extending them with a thermal evolution model. Essentially, coupling of electrochemical–thermal dynamics emerging from the fact that the lithium concentrations contribute to the entropic heat generation is utilized to overcome the observability issue. Then, an estimation scheme comprised of a cascade of a sliding-mode observer and an unscented Kalman filter (UKF) is constructed that exploits the resulting structure of the coupled model. The approach gives new real-time estimation capabilities for two often-sought pieces of information about a battery cell: (1) estimation of cell-capacity and (2) tracking the capacity loss due to degradation mechanisms such as lithium plating. These capabilities are possible since the two-electrode model needs not be reduced further to a single-electrode model by adding Li conservation assumptions, which do not hold with long-term operation. Simulation studies are included for the validation of the proposed scheme. Effect of measurement noise and parametric uncertainties is also included in the simulation results to evaluate the performance of the proposed scheme.

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