Recent Advances in Battery Management System for Li-Ion Type EVs with Predictive Control and FPGA

The increasing acceptance of electric vehicles (EVs) shows the significance of efficient and reliable battery management systems (BMS) for ensuring safety, optimizing the performance, and extend battery life. Various BMS algorithms exists in literature. Model Predictive Control (MPC) is a powerful tool due to its ability to handle multi-variable control processes and incorporate system constraints has also been considered for BMS design. Further, with the high computational demands of MPC, Field Programmable Gate Arrays (FPGAs) are also being integrated to enhance real-time processing capabilities, making BMS solutions for lithium-ion (Li-ion) batteries more effective. In recent times, the Li-ion batteries are considered to be the most suitable for EVs. This fiat has resulted from many years of research and development that improved Li-ion's performance in comparison to Lead-acid, Nickel, Sodium based batteries. Despite these advantages, still there exists some key challenges in terms of safety, cost-effectiveness, longevity, recycling, and degradation. To adhere these concerns, BMS is used in the EVs for proper use of the Li-ion batteries that reduces number of charging and discharging cycles, estimates various health parameters of the battery such as state-of-charge (SoC), state-of-health (SoH), regulates the cell temperature, and computes the degradation. This paper reviews recent advances in BMS using MPC and FPGA, assessing developments in modeling, control strategies, hardware implementation, and testing frameworks. Innovations that address the challenges of state estimation, fault detection, and system efficiency are emphasized for current research and future directions.