Cost-minimization predictive energy management of a postal-delivery fuel cell electric vehicle with intelligent battery State-of-Charge Planner

TL;DR

This paper presents a cost-effective energy management strategy for postal-delivery fuel cell electric vehicles, utilizing data-driven estimators and predictive control to reduce operating costs and enable real-time application.

Contribution

It introduces a novel dual-loop battery state-of-charge estimator and a fuzzy C-means clustering enhanced speed predictor for improved energy management.

Findings

Cost reduction of 4.43% to 7.30% compared to benchmarks.

Real-time computation efficiency with 0.123ms per step.

Effective mitigation of operating costs and power-source degradation.

Abstract

Fuel cell electric vehicles have earned substantial attentions in recent decades due to their high-efficiency and zero-emission features, while the high operating costs remain the major barrier towards their large-scale commercialization. In such context, this paper aims to devise an energy management strategy for an urban postal-delivery fuel cell electric vehicle for operating cost mitigation. First, a data-driven dual-loop spatial-domain battery state-of-charge reference estimator is designed to guide battery energy depletion, which is trained by real-world driving data collected in postal delivery missions. Then, a fuzzy C-means clustering enhanced Markov speed predictor is constructed to project the upcoming velocity. Lastly, combining the state-of-charge reference and the forecasted speed, a model predictive control-based cost-optimization energy management strategy is established to mitigate vehicle operating costs imposed by energy consumption and power-source degradations. Validation results have shown that 1) the proposed strategy could mitigate the operating cost by 4.43% and 7.30% in average versus benchmark strategies, denoting its superiority in term of cost-reduction and 2) the computation burden per step of the proposed strategy is averaged at 0.123ms, less than the sampling time interval 1s, proving its potential of real-time applications.