This paper proposes an efficient nonlinear model predictive control (NMPC) framework to solve nonconvex lithium-ion battery trajectory optimization problems for battery management systems (BMS). It is challenging to solve these problems online due to complexity and nonconvexity. To address these challenges, we combine four established techniques from the control literature. First, we represent the single particle model (SPM) using orthogonal projection techniques. Second, we exploit the differential flatness of Fick’s second law of diffusion to capture all of the dynamics in one electrode using a single scalar trajectory of a “flat output” variable. Third, we optimize the above flat output trajectories using pseudospectral methods. Fourth, we employ the NMPC strategy to solve the battery trajectory optimization problem online. The proposed NMPC framework is demonstrated by solving 2 optimal charging problems accounting for physics-based side reaction constraints and is shown to be twice as computationally efficient as pseudospectral online optimization alone.
- Dynamic Systems and Control Division
Efficient Lithium-Ion Battery Model Predictive Control Using Differential Flatness-Based Pseudospectral Methods
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Liu, J, Li, G, & Fathy, HK. "Efficient Lithium-Ion Battery Model Predictive Control Using Differential Flatness-Based Pseudospectral Methods." Proceedings of the ASME 2015 Dynamic Systems and Control Conference. Volume 1: Adaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Control; Advances in Robotics; Advances in Wind Energy Systems; Aerospace Applications; Aerospace Power Optimization; Assistive Robotics; Automotive 2: Hybrid Electric Vehicles; Automotive 3: Internal Combustion Engines; Automotive Engine Control; Battery Management; Bio Engineering Applications; Biomed and Neural Systems; Connected Vehicles; Control of Robotic Systems. Columbus, Ohio, USA. October 28–30, 2015. V001T13A005. ASME. https://doi.org/10.1115/DSCC2015-9765
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