Multi-Feature Coordinated Adaptive ECMS with Fuzzy Logic for Low-Carbon Sustainable Fuel Cell Hybrid Electric Commercial Vehicles

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Take the MFCA-ECMS method and benchmark against other adaptive strategies or implement in real-time controllers.

🏢実務担当者:Adopt this control algorithm in vehicle ECUs to reduce hydrogen consumption and maintenance costs for fuel cell commercial fleets.

🏛政策担当者:Note that advanced control strategies can significantly improve FCV efficiency and lifespan, strengthening the case for hydrogen infrastructure investment.

This paper introduces a multi-feature coordinated adaptive equivalent consumption minimization strategy (MFCA-ECMS) using fuzzy logic control (FLC) to enhance hydrogen efficiency in fuel cell hybrid electric commercial vehicles (FCHECVs) and extend the lifespan of the fuel cell system (FCS), contributing to sustainable, low-carbon transport. First, a baseline ECMS model is established for the FCHECV, whilst the optimal equivalent factor (EF) is determined using a multi-island genetic algorithm (MIGA) based on representative driving cycles. Second, an adaptive EF framework is developed to overcome the inherent limitation of conventional ECMS—its reliance on a fixed EF—by dynamically integrating three operational features: variation in the battery’s state of charge (SOC), the rate of change in the FCS’s output power, and fluctuations in vehicle power demand. Third, feature-specific adaptive weights are assigned and updated in real time using a fuzzy inference system to regulate the EF online, incorporating multiple features. Simulations are conducted under different initial SOC levels (90% and 45%) across different driving cycles. The results demonstrate that the MFCA-ECMS consistently reduces hydrogen consumption (HC). Compared to the charge-depleting and charge-sustaining (CD-CS) strategy, it achieves HC reductions of 17.98% on the stochastic driving cycle (Random-C) and 18.73% on the urban dynamometer driving schedule (UDDS), outperforming both CD-CS and conventional ECMS in all tested scenarios. Furthermore, the MFCA-ECMS actively suppresses FCS power fluctuations. Regardless of the initial SOC, the proportion of power change rates within the reasonable range exceeds 97%, thereby contributing to extending the FCS lifespan. This reduces emissions and operating costs, enabling sustainable hydrogen-powered commercial vehicle deployment.

• openalex https://doi.org/10.3390/su18136729first seen 2026-07-04 04:54:05