Optimizing Low Pass Filter Cut-off Frequency for Energy Management in Electric Vehicles with Hybrid Energy Storage Systems

Abstract

Mitigating pollution in the transportation sector necessitates the deployment of zeroemission solutions, such as electric vehicles (EVs). One significant challenge with EVs is the limited lifespan of the battery, a key and costly component. To circumvent this issue, a potential solution lies in the integration of batteries with supercapacitors to create a Hybrid Energy Storage System (HESS). This combination can notably decrease the peak current of the battery, thereby prolonging its lifespan, and ultimately, contributing to the long-term cost-effectiveness of EVs. A critical component of the HESS is the Energy Management Strategy (EMS), tasked with optimizing energy distribution. A Low-Pass Filter (LPF) serves as an uncomplicated, real-time EMS. The current study introduces a novel approach for determining the optimal cut-off frequency of the LPF, termed the Ragone Plot with Fine Tuning (RPFT). The Ragone plot provides a general cut-off frequency for the battery and drive cycle, while fine-tuning is employed to optimize it. Simulation results reveal that the RPFT method outperforms the Fast Fourier Transform (FFT) method, thereby proving its efficacy. The application of RPFT resulted in a reduction of battery peak current and battery current root mean square (BCRMS) by up to 29.80% and 9.99%, respectively. This study offers valuable insights for improving energy management in electric vehicles and underscores the potential of the RPFT method in extending battery lifespan and enhancing the costeffectiveness of EVs.

Energy Management in Hybrid Electric and Hybrid Energy Storage System Vehicles: A Fuzzy Logic Controller Review

“…According to [15], battery is a high-energy type ESS which enables longer vehicle traveling distance, and SC is the highpower type ESS which can handle high load power in the acceleration phase. Combining the strengths of batteries and supercapacitors, the Hybrid Energy Storage System (HESS) is proposed.…”

“…However, both of these do not focus on specific EMS methods but discuss a lot of EMS used in hybrid vehicles. Some studies do indeed review EMS methods such as lowpass filtering (LPF) [23], model predictive control (MPC) [30][31] [32], equivalent consumption minimization strategy (ECMS) [26], and reinforcement learning (RL) [33]. On the other hand, as the authors know in the Scopus database, no article discusses and reviews Fuzzy Logic Controller (FLC) as EMS.…”