A thermochemical recuperation (TCR) reactor was developed and experimentally evaluated with the objective to improve dual-fuel diesel–ammonia compression ignition engines. The novel system simultaneously decomposed ammonia into a hydrogen-containing mixture to allow high diesel fuel replacement ratios and oxidized unburned ammonia emissions in the exhaust, overcoming two key shortcomings of ammonia combustion in engines from the previous literature. In the experimental work, a multi-cylinder compression ignition engine was operated in dual-fuel mode using intake-fumigated ammonia and hydrogen mixtures as the secondary fuel. A full-scale catalytic TCR reactor was constructed and generated the fuel used in the engine experiments. The results show that up to 55% of the total fuel energy was provided by ammonia on a lower heating value basis. Overall engine brake thermal efficiency increased for modes with a high exhaust temperature where ammonia decomposition conversion in the TCR reactor was high but decreased for all other modes due to poor combustion efficiency. Hydrocarbon and soot emissions were shown to increase with the replacement ratio for all modes due to lower combustion temperatures and in-cylinder oxidation processes in the late part of heat release. Engine-out oxides of nitrogen (NO_x) emissions decreased with increasing diesel replacement levels for all engine modes. A higher concentration of unburned ammonia was measured in the exhaust with increasing replacement ratios. This unburned ammonia predominantly oxidized to NO_x species over the oxidation catalyst used within the TCR reactor. Ammonia substitution thus increased post-TCR reactor ammonia and NO_x emissions in this work. The results show, however, that engine-out NH₃-to-NO_x ratios were suitable for passive selective catalytic reduction, thus demonstrating that both ammonia and NO_x from the engine could be readily converted to N₂ if the appropriate catalyst were used in the TCR reactor.

Range prediction is a standard feature in most modern road vehicles, allowing drivers to make informed decisions about when to refuel. Most vehicles make range predictions through data- or model-driven means, monitoring the average fuel consumption rate or using a tuned vehicle model to predict fuel consumption. The uncertainty of future driving conditions makes the range prediction problem challenging, particularly for less pervasive battery electric vehicles (BEV). Most contemporary machine learning-based methods attempt to forecast the battery SOC discharge profile to predict vehicle range. In this work, we propose a novel approach using two recurrent neural networks (RNNs) to predict the remaining range of BEVs and the minimum charge required to safely complete a trip. Each RNN has two outputs that can be used for statistical analysis to account for uncertainties; the first loss function leads to mean and variance estimation (MVE), while the second results in bounded interval estimation (BIE). These outputs of the proposed RNNs are then used to predict the probability of a vehicle completing a given trip without charging, or if charging is needed, the remaining range and minimum charging required to finish the trip with high probability. Training data was generated using a low-order physics model to estimate vehicle energy consumption from historical drive cycle data collected from medium-duty last-mile delivery vehicles. The proposed method demonstrated high accuracy in the presence of day-to-day route variability, with the root-mean-square error (RMSE) below 6% for both RNN models.

Anhydrous ammonia (NH₃) use in internal combustion engines represents a zero-carbon energy solution that is fully sustainable if NH₃ is generated renewably. An active hydrogen-fueled pre-chamber to induce turbulent jet ignition is investigated in this work as a means to enhance ignition energy and turbulent flame speed in an NH₃ fueled engine. The strength of the turbulent jets, and thus their effectiveness in igniting the main-chamber and enhancing combustion, is highly dependent on pre-chamber equivalence ratio and hydrogen fraction. Local pre-chamber mixtures are varied in the present study by investigating a range of pre-mixed intake NH₃-air equivalence ratios (ϕ = 0.5–1) under a consistent hydrogen direct injection strategy in the pre-chamber. Additionally, given the knock-resistance of NH₃, multiple compression ratios were studied to investigate the impact on efficiency, emissions, and the combustion process. Results show a clear trade-off where leaner intake equivalence ratios enhance the reactivity of the pre-chamber (greater local hydrogen fraction and closer to stoichiometry) but reduce the reactivity of the main-chamber (lean and slow flame speed). Spark timing optimizes the trade-off under a fixed injection strategy; advancing spark provides more time for combustion to occur in the main-chamber but inhibits pre-chamber reactivity for a less energetic ignition of the main chamber. Optimal indicated thermal efficiency and minimum unburned NH₃ and N₂O emissions occur around 0.7–0.8 equivalence ratio for all compression ratios. Conversely, NO_x is highest at these equivalence ratios but could theoretically be eliminated using selective catalytic reduction aftertreatment using the NH₃ present in the exhaust.