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Abstract
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.
Highlights
The adoption of alternative fuel vehicles is considered as one of the major steps towards addressing the issues related to oil dependence, air pollution, and most importantly climate change
Both from the government and the private sector around the world, to enhance the usage of electric vehicles (EVs), we continue to face many challenges to promote the wider acceptance of EVs by the general public
The fundamental operators such as add, subtract, multiply, divide, compare, and square root are at the lowest level; the vector and matrix operations including matrix multiplication/addition/subtraction are at the level; the four stages of the model predictive controller (MPC), i.e., model generation, optimal solution, Hildreth’s quadratic programming (QP) process, and state and plant generation, are at the third level of the design hierarchy; and the MPC is at the highest level
Summary
The adoption of alternative fuel vehicles is considered as one of the major steps towards addressing the issues related to oil dependence, air pollution, and most importantly climate change. Electricity and hydrogen fuel cells are the top contenders for the alternative fuel for vehicles Despite numerous initiatives, both from the government and the private sector around the world, to enhance the usage of electric vehicles (EVs), we continue to face many challenges to promote the wider acceptance of EVs by the general public. Both from the government and the private sector around the world, to enhance the usage of electric vehicles (EVs), we continue to face many challenges to promote the wider acceptance of EVs by the general public Some of these major challenges include charging time of the battery and the maximum driving distance of the vehicle [1]. It is crucial to investigate and provide efficient techniques and design methodologies, to monitor and control the battery packs at cell levels and to optimize the parameters of the individual cells, in order to enhance the durability and useful life of the battery packs
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