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In the era of climate change, and with the rapid depletion of fossil resources, efficient and sustainable transportation systems, such as hybrid electric vehicles (HEVs), are becoming imperative. The cornerstone of HEVs is the efficient battery technology, in order to extend the useful life of the battery and to provide improved performance to fossil fuel technology. Model predictive control (MPC) is an effective technique for battery management systems (BMS), which enables cell-level monitoring and controlling of the battery pack, reducing the safety margin of operation, while maintaining the safety requirements. Furthermore, utilizing the physics-based model (PBM) of the battery allows the control system to operate on the chemical and physical process of the battery that are the root cause of battery degradation. The non-linear extended Kalman filter (EKF) serves as the state observer to monitor the physical and chemical properties/processes of each battery cell, since these are internal processes of the battery, making them physically unobservable. The amalgamation of the aforementioned techniques, i.e., MPC, PBM, and EKF, can extend the useful life of the battery cell/pack, especially for lithium-ion batteries. In real-world scenarios, HEVs and smart applications often require portable BMS, compelling BMS to be executed on highly constrained embedded devices. Also, the inherent adaptive control process of physics-based (PB) MPC is uniquely suited for smart systems/applications. However, the high computational complexity of PB-MPC, comprising PB-EKF, prevents it from being realized on resource-constrained embedded devices, making it infeasible for portable BMS. In this research work, we introduce novel, unique, and efficient FPGA-based embedded hardware accelerator for PB-MPC smart sensor (comprising PB-EKF) for BMS, specifically on embedded devices, by addressing the computational complexity of PBM. Our proposed embedded PB-MPC hardware accelerator achieved 58 times speedup compared to its embedded software counterpart, while maintain a small footprint required for portable systems. This speedup enables us to manage more battery cells utilizing a single chip compared to that of embedded processor-based solutions.

Towards Composing Efficient FPGA-Based Hardware Accelerators for Physics-Based Model Predictive Control Smart Sensor for HEV Battery Cell Management