A Comparative Evaluation of Rule-Based Strategies, ECMSs, and MPC Strategies for Fuel Cell Hybrid LCV Energy Management

Energy Management Strategies (EMSs) are crucial for enhancing fuel economy and reducing emissions in light commercial vehicles (LCVs). This paper presented three EMS approaches for LCVs with hybrid powertrains: Rule-Based Control (RBC) and two optimization-based strategies, the Equivalent Consumption Minimization Strategy (ECMS) and Model Predictive Control (MPC). To enhance robustness under varying operating conditions, optimization algorithms were designed and tuned using the WLTC City driving cycle, and adaptive components were included. For a fair assessment of overall efficiency, all strategies were compared under identical constraints on hydrogen and electrical energy consumption. The results showed that, under these constraints, MPC achieved the longest driving distance, highlighting its superior energy utilization capability. In a broader comparative analysis, both the ECMS and MPC outperformed the benchmark RBC, with MPC demonstrating the most consistent performance, enhanced stability, and strong adaptability in dynamic scenarios. The findings indicate that MPC offers notable advantages for LCV energy management, combining efficiency, robustness, and interpretability, positioning it as a promising candidate for practical implementation in future hybrid powertrain systems.

Authors

Institutions

Publication Details

Journal
World Electric Vehicle Journal
Published
2026-03-23
DOI
https://doi.org/10.3390/wevj17030163
Primary Topic
Electric and Hybrid Vehicle Technologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Comparative Evaluation of Rule-Based Strategies, ECMSs, and MPC Strategies for Fuel Cell Hybrid LCV Energy Management

Henrique de Carvalho Pinheiro, Elia Grano, Massimiliana Carello, Zihao Guo
World Electric Vehicle Journal
Electric and Hybrid Vehicle Technologies
article

A Comparative Evaluation of Rule-Based Strategies, ECMSs, and MPC Strategies for Fuel Cell Hybrid LCV Energy Management

Henrique de Carvalho Pinheiro, Elia Grano, Massimiliana Carello, Zihao Guo
article en

Abstract

Energy Management Strategies (EMSs) are crucial for enhancing fuel economy and reducing emissions in light commercial vehicles (LCVs). This paper presented three EMS approaches for LCVs with hybrid powertrains: Rule-Based Control (RBC) and two optimization-based strategies, the Equivalent Consumption Minimization Strategy (ECMS) and Model Predictive Control (MPC). To enhance robustness under varying operating conditions, optimization algorithms were designed and tuned using the WLTC City driving cycle, and adaptive components were included. For a fair assessment of overall efficiency, all strategies were compared under identical constraints on hydrogen and electrical energy consumption. The results showed that, under these constraints, MPC achieved the longest driving distance, highlighting its superior energy utilization capability. In a broader comparative analysis, both the ECMS and MPC outperformed the benchmark RBC, with MPC demonstrating the most consistent performance, enhanced stability, and strong adaptability in dynamic scenarios. The findings indicate that MPC offers notable advantages for LCV energy management, combining efficiency, robustness, and interpretability, positioning it as a promising candidate for practical implementation in future hybrid powertrain systems.

World Electric Vehicle JournalVol. 17(3)
Politecnico di Torino (IT)
Affordable and clean energy
Openalex Percentile: Top 12%
Electric and Hybrid Vehicle Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.