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With the ever-growing concerns about carbon emissions and air pollution throughout the world, electric vehicles (EVs) are one of the most viable options for clean transportation. EVs are typically powered by a battery pack such as lithium-ion, which is created from a large number of individual cells. In order to enhance the durability and prolong the useful life of the battery pack, it is imperative to monitor and control the battery packs at the cell level. Model predictive controller (MPC) is considered as a feasible technique for cell-level monitoring and controlling of the battery packs. For instance, the fast-charge MPC algorithm keeps the Li-ion battery cell within its optimal operating parameters while reducing the charging time. In this case, the fast-charge MPC algorithm should be executed on an embedded platform mounted on an individual cell; however, the existing algorithm for this technique is designed for general-purpose computing. In this research work, we introduce novel, unique, and efficient embedded hardware and software architectures for the fast-charge MPC algorithm, considering the constraints and requirements associated with the embedded devices. We create two unique hardware versions: register-based and memory-based. Experiments are performed to evaluate and illustrate the feasibility and efficiency of our proposed embedded architectures. Our embedded architectures are generic, parameterized, and scalable. Our hardware designs achieved 100 times speedup compared to its software counterparts.

Efficient embedded architectures for fast-charge model predictive controller for battery cell management in electric vehicles