Energy Management of Hybrid Electric Vehicle Considering Battery and Fuel Cell Parameters Using Multi-Objective Optimization for Dynamic Driving Cycles

Abstract

A good strategy for energy management is essential to control the power distribution between fuel cells and batteries in hybrid electric cars. Various energy management systems have been explored in the literature, focusing on optimizing the fuel cell characteristics. The literature review reveals researchers have not adequately addressed the effect of key battery parameters for developing energy management strategies for realistic driving conditions. This research proposes a novel energy management strategy with a multi-objective optimization for fuel cell battery hybrids, focusing on fuel efficiency, energy utilization, and drivability. Energetic macroscopic representation is a framework for powertrain modeling, aiding in creating the energy management system (EMS). The main goal is to provide a systematic control framework that integrates local bus voltage and traction control controllers with a global controller for energy management systems. The unique EMS regulates power flows by dynamically modifying battery and fuel cell operation's rate limitations and saturation levels. The thresholds for rate restriction and saturation are optimized offline using the multi-objective optimization. The impact of optimization parameters on the optimization goals is examined using three standard driving cycles. The simulation findings demonstrate that the efficacy of local controllers is contingent upon the driving cycle. Battery management excels in low dynamic power cycles, whereas fuel cell management is superior in high constant power cycles. The EMS may allocate power between the battery and the fuel cell, allowing the battery to manage transients. Altering the operational restrictions modifies the power distribution ratio while meeting the power requirements. Restricting battery power improves battery longevity by 50%. The modification of weights among the optimization targets is also taken into account. Conversely, a greater emphasis on reducing gasoline usage undermines battery energy. Minimization of power errors or drivability is prioritized above everything else. The results demonstrate that the suggested method can function effectively with an accuracy of 91% relative to optimal circumstances. The energy distribution between the battery and fuel cell enhances the longevity of both power sources.