Energy efficiency analysis of ammonia-fueled power systems for vehicles considering residual heat recovery

TL;DR

This paper analyzes the energy efficiency of ammonia-fueled vehicle power systems by modeling and simulating three configurations, emphasizing residual heat recovery to optimize system performance.

Contribution

It introduces detailed models for three ammonia-based power systems and evaluates how residual heat recovery impacts overall efficiency and design optimization.

Findings

Residual heat recovery significantly improves system efficiency.

System configuration and parameters critically influence energy performance.

Guidelines for system design and optimization are proposed.

Abstract

Ammonia, known as a good hydrogen carrier, shows great potential for use as a zero-carbon fuel for vehicles. However, both the internal combustion engine (ICE) and the proton exchange membrane fuel cell (PEMFC), the currently available engines used by the vehicle, require hydrogen decomposed from ammonia. On-board hydrogen production is an energy-intensive process that significantly reduces system efficiency. Therefore, energy recovery from the system's residual heat is essential to promote system efficiency. ICEs and FCs require different amounts of hydrogen, and they produce residual heat of different quality and quantity, so the system efficiency is not only determined by the engine operating point, but also by the measures and ratios of residual heat recovery. To thoroughly understand the relationships between system energy efficiency and system configuration as well as system parameters, this paper takes three typical power systems with different configurations as our objects. Models of three systems are set up for system energy efficiency analysis, and carry out simulations under different conditions to conduct system output power and energy efficiency. By analyzing the simulation results, the factors that most significantly impact the system efficiency are identified, the guidelines for system design and parameter optimization are proposed.