A Cell-in-the-Loop Approach to Systems Modelling and Simulation of Energy Storage Systems

This research is aligned with the engineering challenge of scaling-up individual battery cells into a complete energy storage system (ESS). Manufacturing tolerances, coupled with thermal gradients and the differential electrical loading of adjacent cells, can result in significant variations in the rate of cell degradation, energy distribution and ESS performance. The uncertain transition from cell to system often manifests itself in over-engineered, non-optimal ESS designs within both the transport and energy sectors. To alleviate these issues, the authors propose a novel model-based framework for cell-in-the-loop simulation (CILS) in which a physical cell may be integrated within a complete model of an ESS and exercised against realistic electrical and thermal loads in real-time. This paper focuses on the electrical integration of both real and simulated cells within the CILS test environment. Validation of the CILS approach using real-world electric vehicle data is presented for an 18650 cell. The cell is integrated within a real-time simulation model of a series string of similar cells in a 4sp1 configuration. Results are presented that highlight the impact of cell variability (i.e., capacity and impedance) on the energy available from the multi-cell system and the useable capacity of the physical cell.

[1]  Richard D. Braatz,et al.  Modeling and Simulation of Lithium-Ion Batteries from a Systems Engineering Perspective , 2010 .

[2]  Jin Wang,et al.  Study on Global Optimization and Control Strategy Development for a PHEV Charging Facility , 2012, IEEE Transactions on Vehicular Technology.

[3]  Stephen R. Duncan,et al.  Advanced battery management systems using fast electrochemical modelling , 2013 .

[4]  James Marco,et al.  A Novel Method for the Parameterization of a Li-Ion Cell Model for EV/HEV Control Applications , 2012, IEEE Transactions on Vehicular Technology.

[5]  James Marco,et al.  Current Variation in Parallelized Energy Storage Systems , 2014, 2014 IEEE Vehicle Power and Propulsion Conference (VPPC).

[6]  Remus Teodorescu,et al.  Accelerated Lifetime Testing Methodology for Lifetime Estimation of Lithium-Ion Batteries Used in Augmented Wind Power Plants , 2014 .

[7]  Joeri Van Mierlo,et al.  Standardization Work for BEV and HEV Applications: Critical Appraisal of Recent Traction Battery Documents , 2012 .

[8]  N A Chaturvedi,et al.  Modeling, estimation, and control challenges for lithium-ion batteries , 2010, Proceedings of the 2010 American Control Conference.

[9]  Hongwen He,et al.  Energy management strategy research on a hybrid power system by hardware-in-loop experiments , 2013 .

[10]  Giovanni Fiengo,et al.  Comparison of reduced order lithium-ion battery models for control applications , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[11]  N. Omar,et al.  Lithium iron phosphate based battery: Assessment of the aging parameters and development of cycle life model , 2014 .

[12]  Wei Liu,et al.  Power Capability Testing of a Lithium-ion Battery Using Hardware in the Loop , 2010 .

[13]  Hongwen He,et al.  Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach , 2011 .

[14]  Hans-Georg Herzog,et al.  Battery emulation considering thermal behavior , 2011, 2011 IEEE Vehicle Power and Propulsion Conference.

[15]  Jace Allen Simulation and Test Systems for Validation of Electric Drive and Battery Management Systems , 2012 .

[16]  Jie Wu,et al.  Large-Scale Energy Storage System Design and Optimization for Emerging Electric-Drive Vehicles , 2013, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[17]  James Marco,et al.  The Control-Oriented Design and Simulation of a High Voltage Bus Management Strategy for Use within Hybrid Electric Vehicles , 2007 .

[18]  Krishnan S. Hariharan A coupled nonlinear equivalent circuit – Thermal model for lithium ion cells , 2013 .

[19]  Jianqiu Li,et al.  A review on the key issues for lithium-ion battery management in electric vehicles , 2013 .

[20]  Zechang Sun,et al.  Cell-BMS validation with a hardware-in-the-loop simulation of lithium-ion battery cells for electric vehicles , 2013 .

[21]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification , 2004 .

[22]  Christian Fleischer,et al.  Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles , 2014 .

[23]  Matthew B. Pinson,et al.  Internal resistance matching for parallel-connected lithium-ion cells and impacts on battery pack cycle life , 2014 .

[24]  Jean-Christophe Crebier,et al.  Multi-cell battery emulator for advanced battery management system benchmarking , 2011, 2011 IEEE International Symposium on Industrial Electronics.

[25]  Xiaosong Hu,et al.  A comparative study of equivalent circuit models for Li-ion batteries , 2012 .

[26]  Matthieu Dubarry,et al.  State-of-charge estimation and uncertainty for lithium-ion battery strings , 2014 .

[27]  Bor Yann Liaw,et al.  A novel on-board state-of-charge estimation method for aged Li-ion batteries based on model adaptive extended Kalman filter , 2014 .

[28]  Poria Fajri,et al.  Development of an Experimental Testbed for Research in Lithium-Ion Battery Management Systems , 2013 .

[29]  Matthieu Dubarry,et al.  From single cell model to battery pack simulation for Li-ion batteries , 2009 .

[30]  Shaahin Filizadeh,et al.  Battery characterization for vehicular applications using hardware-in-loop real-time simulation , 2013, 2013 3rd International Conference on Electric Power and Energy Conversion Systems.

[31]  Christian Calvillo,et al.  Capacity fade and aging models for electric batteries and optimal charging strategy for electric vehicles , 2013 .

[32]  Nigel P. Brandon,et al.  Coupled thermal–electrochemical modelling of uneven heat generation in lithium-ion battery packs , 2013 .

[33]  Billy Wu,et al.  The effect of thermal gradients on the performance of battery packs in automotive applications , 2013 .

[34]  Hongwen He,et al.  Online Estimation of Peak Power Capability of Li-Ion Batteries in Electric Vehicles by a Hardware-in-Loop Approach , 2012 .

[35]  Ken Darcovich,et al.  Modelling the impact of variations in electrode manufacturing on lithium-ion battery modules , 2012 .

[36]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 1. Background , 2004 .

[37]  Bor Yann Liaw,et al.  Improved extended Kalman filter for state of charge estimation of battery pack , 2014 .

[38]  Wei Liu,et al.  Battery algorithm verification and development using hardware-in-the-loop testing , 2010 .

[39]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation , 2004 .

[40]  Pierluigi Pisu,et al.  Nonlinear Robust Observers for State-of-Charge Estimation of Lithium-Ion Cells Based on a Reduced Electrochemical Model , 2015, IEEE Transactions on Control Systems Technology.

[41]  Shriram Santhanagopalan,et al.  Quantifying Cell-to-Cell Variations in Lithium Ion Batteries , 2012 .

[42]  Zhenwei Cao,et al.  A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles , 2014 .

[43]  Kan Chen,et al.  Multiphysics Test Bed for Renewable Energy Systems in Smart Homes , 2013, IEEE Transactions on Industrial Electronics.

[44]  Andrew McGordon,et al.  Simulation methodologies to support novel fuse design for energy storage systems using COMSOL , 2013 .

[45]  Rolf Findeisen,et al.  Electrochemical Model Based Observer Design for a Lithium-Ion Battery , 2013, IEEE Transactions on Control Systems Technology.

[46]  Michael Pecht,et al.  Battery Management Systems in Electric and Hybrid Vehicles , 2011 .