DSCC 2015-9705 BATTERY CHARGE CONTROL WITH AN ELECTROTHERMAL-AGING COUPLING

Efficient and safe battery charge control is an important prerequisite for large-scale deployment of clean energy systems. This paper proposes an innovative approach to devising optimally health-conscious fast-safe charge protocols. A multiobjective optimal control problem is mathematically formulated via a coupled electro-thermal-aging battery model, where electrical and aging sub-models depend upon the core temperature captured by a two-state thermal sub-model. The LegendreGauss-Radau (LGR) pseudo-spectral method with adaptive multi-mesh-interval collocation is employed to solve the resulting highly nonlinear six-state optimal control problem. Charge time and health degradation are therefore optimally traded off, subject to both electrical and thermal constraints. Minimumtime, minimum-aging, and balanced charge scenarios are examined in detail. The implications of the upper voltage bound, ambient temperature, and cooling convection resistance to the optimization outcome are investigated as well.

[1]  M. Verbrugge,et al.  Cycle-life model for graphite-LiFePO 4 cells , 2011 .

[2]  P. T. Krein,et al.  Review of Battery Charger Topologies, Charging Power Levels, and Infrastructure for Plug-In Electric and Hybrid Vehicles , 2013, IEEE Transactions on Power Electronics.

[3]  Xiaosong Hu,et al.  Comparison of Three Electrochemical Energy Buffers Applied to a Hybrid Bus Powertrain With Simultaneous Optimal Sizing and Energy Management , 2014, IEEE Transactions on Intelligent Transportation Systems.

[4]  Z. Ullah,et al.  Fast intelligent battery charging: neural-fuzzy approach , 1995 .

[5]  Xiaosong Hu,et al.  Longevity-conscious dimensioning and power management of the hybrid energy storage system in a fuel cell hybrid electric bus , 2015 .

[6]  Rolf Findeisen,et al.  Optimal charging strategies in lithium-ion battery , 2011, Proceedings of the 2011 American Control Conference.

[7]  William W. Hager,et al.  Direct trajectory optimization and costate estimation of finite-horizon and infinite-horizon optimal control problems using a Radau pseudospectral method , 2011, Comput. Optim. Appl..

[8]  Yi-Hwa Liu,et al.  Search for an Optimal Rapid-Charging Pattern for Li-Ion Batteries Using the Taguchi Approach , 2010, IEEE Transactions on Industrial Electronics.

[9]  Hartmut Surmann,et al.  Genetic optimization of a fuzzy system for charging batteries , 1996, IEEE Trans. Ind. Electron..

[10]  B. Egardt,et al.  Enhanced Sample Entropy-based Health Management of Li-ion Battery for Electrified Vehicles , 2014 .

[11]  Anil V. Rao,et al.  GPOPS-II , 2014, ACM Trans. Math. Softw..

[12]  Joeri Van Mierlo,et al.  Lithium-ion batteries: Evaluation study of different charging methodologies based on aging process , 2015 .

[13]  Krishna R. Pattipati,et al.  Battery charging optimization for OCV-resistance equivalent circuit model , 2015, 2015 American Control Conference (ACC).

[14]  Liang-Rui Chen,et al.  A Design of a Grey-Predicted Li-Ion Battery Charge System , 2008, IEEE Transactions on Industrial Electronics.

[15]  Kang Xu,et al.  Study of the charging process of a LiCoO2-based Li-ion battery , 2006 .

[16]  Xiaosong Hu,et al.  Charging time and loss optimization for LiNMC and LiFePO4 batteries based on equivalent circuit models , 2013 .

[17]  Richard D. Braatz,et al.  Real-time model predictive control for the optimal charging of a lithium-ion battery , 2015, 2015 American Control Conference (ACC).

[18]  Shengbo Zhang.  The effect of the charging protocol on the cycle life of a Li-ion battery , 2006 .

[19]  William W. Hager,et al.  Pseudospectral methods for solving infinite-horizon optimal control problems , 2011, Autom..

[20]  Richard D. Braatz,et al.  Optimum Charging Profile for Lithium-Ion Batteries to Maximize Energy Storage and Utilization , 2010 .

[21]  Jason B. Siegel,et al.  A lumped-parameter electro-thermal model for cylindrical batteries , 2014 .

[22]  Miroslav Krstic,et al.  PDE estimation techniques for advanced battery management systems — Part I: SOC estimation , 2012, 2012 American Control Conference (ACC).

[23]  David Anseán,et al.  Fast charging technique for high power lithium iron phosphate batteries: A cycle life analysis , 2013 .

[24]  Matthew P. Castanier,et al.  Parameterization and Validation of an Integrated Electro-Thermal Cylindrical LFP Battery Model , 2012 .

[25]  M. Krstić,et al.  Adaptive Partial Differential Equation Observer for Battery State-of-Charge/State-of-Health Estimation Via an Electrochemical Model , 2014 .

[26]  Ardalan Vahidi,et al.  Maximizing charging efficiency of lithium-ion and lead-acid batteries using optimal control theory , 2015, 2015 American Control Conference (ACC).

[27]  W. Hager,et al.  An hp‐adaptive pseudospectral method for solving optimal control problems , 2011 .

[28]  Lino Guzzella,et al.  Battery State-of-Health Perceptive Energy Management for Hybrid Electric Vehicles , 2012, IEEE Transactions on Vehicular Technology.

[29]  S. Moura,et al.  Enhanced Performance of Li-Ion Batteries via Modified Reference Governors and Electrochemical Models , 2015, IEEE/ASME Transactions on Mechatronics.

[30]  I. Michael Ross,et al.  A review of pseudospectral optimal control: From theory to flight , 2012, Annu. Rev. Control..

[31]  Yu-Chung Lin,et al.  Search for an optimal rapid charging pattern for lithium-ion batteries using ant colony system algorithm , 2005, IEEE Transactions on Industrial Electronics.

[32]  Anil V. Rao,et al.  Optimal control of Formula One car energy recovery systems , 2014, Int. J. Control.