Optimal charging of an electric vehicle battery pack: A real-time sensitivity-based model predictive control approach

JOURNAL OF POWER SOURCES

卷:461

Article

Pozzi, Andrea¹  Torchio, Marcello²  Braatz, Richard D.³  Raimondo, Davide M.¹ 

[1] Univ Pavia, Via Ferrata 5, Pavia 27100, Italy

[2] United Technol Res Ctr Ireland Ltd, 2nd Floor Penrose Business Ctr, Cork, Ireland

[3] MIT, 77 Massachusetts Ave, Cambridge, MA 02139 USA

关键词: Lithium-ion batteries;    Battery management systems;    Advanced battery management systems;    Model predictive control;    Predictive control;   

DOI  :  10.1016/j.jpowsour.2020.228133

来源: Elsevier

【 摘 要 】

Lithium-ion battery packs are usually composed of hundreds of cells arranged in series and parallel connections. The proper functioning of these complex devices requires suitable Battery Management Systems (BMSs). Advanced BMSs rely on mathematical models to assure safety and high performance. While many approaches have been proposed for the management of single cells, the control of multiple cells has been less investigated and usually relies on simplified models such as equivalent circuit models. This paper addresses the management of a battery pack in which each cell is explicitly modelled as the Single Particle Model with electrolyte and thermal dynamics. A nonlinear Model Predictive Control (MPC) is presented for optimally charging the battery pack while taking voltage and temperature limits on each cell into account. Since the computational cost of nonlinear MPC grows significantly with the complexity of the underlying model, a sensitivity-based MPC (sMPC) is proposed, in which the model adopted is obtained by linearizing the dynamics along a nominal trajectory that is updated over time. The resulting sMPC optimizations are quadratic programs which can be solved in real-time even for large battery packs (e.g. fully electric motorbike with 156 cells) while achieving the same performance of the nonlinear MPC.

【 授权许可】

Free