Model Predictive Iterative Learning Control Design for Battery Optimal Electro-Thermal Management Under Daily-Variant State-of-Charge Patterns

• Published in: IEEE access Vol. 12; pp. 155904 - 155914
• Main Author: Nguyen, Dinh Hoa
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Batteries; Control systems design; Controllers; Cooling; Electric charge; Electric vehicle charging; iteration-varying references; Iterative learning control; Iterative methods; Learning; Li-ion battery electro-thermal model; Lithium-ion batteries; model predictive control; Ohmic dissipation; Optimization; Predictive control; Predictive models; Public transportation; Resistance heating; State of charge; Temperature distribution; Thermal management; Tracking errors; Upper bounds; Vectors
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3482319

This research studies the control of electro-thermal dynamics of cylindrical Li-ion battery packs in electric vehicles (EVs). These dynamics are coupled by the charging-discharging current which generates the Joule heating that directly affects to the operation of battery cells. Hence, to guarantee the battery cells' temperatures in a desired range for their best operation, a model predictive iterative learning control (MPILC) design is proposed, which composes of an iterative learning controller (ILC) and a model predictive controller (MPC). The former controller with iteration-varying learning gains helps better track slightly variant daily state-of-charge (SoC) patterns of the battery pack. A constant upper bound is derived for the tracking error norm, based on which the iteration-varying learning gains can be designed to make the tracking error converge to zero. The latter controller employs the result of the former as a predicted disturbance to design the cooling-heating temperature input for the battery pack by minimizing its consumed energy while driving the battery cells' temperatures to a desired range. Simulations are then carried out to illustrate the effectiveness of the proposed MPILC design in tracking daily-variant SoC profiles while guaranteeing battery cells' temperatures in expected intervals.