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 battery technology is imperative for the adoption of clean energy automotive solutions. In addition, efficient battery technology extends the useful life of the battery as well as provides improved performance to fossil fuel technology. Model predictive control (MPC) is an effective way to operate battery management systems (BMS) at their maximum capability, while maintaining the safety requirements. Using the physics-based model (PBM) of the battery allows the control system to operate on the chemical and physical process of the battery. Since these processes are internal to the battery and are physically unobservable, the extended Kalman filter (EKF) serves as a virtual observer that can monitor the physical and chemical properties that are otherwise unobservable. These three methods (i.e., PBM, EKF, and MPC) together can prolong the useful life of the battery, especially for Li-ion batteries. This capability is not limited to the automotive industry: any real-world smart application can benefit from a portable/mobile efficient BMS, compelling these systems to be executed on resource-constrained embedded devices. Furthermore, the intrinsic adaptive control process of the PBM is uniquely suited for smart systems and smart technology. However, the sheer computational complexity of PBM for MPC and EKF prevents it from being realized on highly constrained embedded devices. In this research work, we introduce a novel, unique, and efficient embedded software architecture for a PB-EKF-MPC smart sensor for BMS, specifically on embedded devices, by addressing the computational complexity of PBM. Our proposed embedded software architecture is created in such a way to be executed on a 32-bit embedded microprocessor running at 100 MHz with a limited memory of 128 KB, and still obtains an average execution time of 4.8 ms.
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