Fuel Economy Improvement Research Articles

Lean-Stratified Combustion System with Miller Cycle for Downsized Boosted Application - Part 2

<div class="section abstract"><div class="htmlview paragraph">Automotive manufacturers relentlessly explore engine technology combinations to achieve reduced fuel consumption under continued regulatory, societal and economic pressures. For example, technologies enabling advanced combustion modes, increased expansion to effective compression ratio and reduced parasitics continue to be developed and integrated within conventional and hybrid propulsion strategies across the industry. A high-efficiency gasoline engine capable for use in conventional or hybrid electric vehicle platforms is highly desirable. This paper is the second of two papers describing the multi-cylinder integration of a technology package combining lean-stratified combustion with Miller cycle for downsized boosted applications. The first paper describes the design, analysis and single-cylinder testing conducted to down-select the combustion system deployed to the multi-cylinder engine. This paper defines a light-duty engine package capable of achieving a 35% fuel economy improvement at US Tier 3 emission standards over a naturally aspirated stoichiometric baseline vehicle. A multi-mode combustion system was developed enabling highly efficient lean-stratified operation at light-load and stoichiometric Miller-cycle operation at mid- to high-loads. A central direct-injection four-valve layout was designed with high-tumble ports and a bowl-in-piston capable of stable operation under highly dilute mixtures when properly matched to fuel spray characteristics, injection strategy, and a high-energy ignition system. Boost system layout and base engine architecture were selected to minimize parasitics and maximize aftertreatment effectiveness. A multi-cylinder engine was developed and calibrated over a steady-state map. Results fed vehicle simulations over the EPA city and highway test cycles to predict the fuel economy improvement and tailpipe emissions potential. Fuel economy improvements over the baseline vehicle were projected at 42% and 39% for active-urea and passive-ammonia SCR aftertreatment systems respectively. Controls complexity to achieve regulatory compliance and the cost of the lean aftertreatment system were identified as the primary challenges to commercial viability.</div></div>

Downsized Boosted Dilute Combustion, Exhaust Compounded (DBDC+EC) Experimental Engine Design, Thermodynamic Model Comparison, and Performance Potential Predictions

<div class="section abstract"><div class="htmlview paragraph">An experimental piston compounded engine was designed with guidance from thermodynamic modeling, then was built and tested to compare the model predictions to measured results. The piston-compounded concept has shown great potential for improvements in efficiency over current state-of-the-art light-duty engines through the use of an efficient second expansion process to more fully recover energy still present in the exhaust gasses, and was further developed into the Downsized Boosted Dilute Combustion, Exhaust Compounded (DBDC+EC) engine presented here. This paper documents some of the more unique design elements of this engine as well as a performance comparison between test data and modeling expectations. Ultimately, an experimental stoichiometric spark-ignited piston compounded engine was designed, five blocks were built, and collectively they were run for thousands of hours. The experimental engines achieved a minimum of 222 g/kW-hr BSFC with a wide region of operation under 250 g/kW-hr, and the performance matched predictions from a thermodynamic model that reflected the as-built hardware. Predictions for a 2nd generation DBDC+EC engine result in a 10% fuel economy improvement over a similar downsized boosted stoichiometric SI engine, with an additional 9% improvement for lean operation at low load. These result in minimum BSFCs of 212 and 206 g/kW-hr for the stoichiometric and lean DBD+EC Gen 2 engines respectively.</div></div>

Lean-Stratified Combustion System with Miller Cycle for Downsized Boosted Application - Part I

<div class="section abstract"><div class="htmlview paragraph">Automotive manufacturers relentlessly explore engine technology combinations to achieve reduced fuel consumption under continued regulatory, societal and economic pressures. For example, technologies enabling advanced combustion modes, increased expansion to effective compression ratio, and reduced parasitics continue to be developed and integrated within conventional and hybrid propulsion strategies across the industry. A high-efficiency gasoline engine capable for use in conventional or hybrid electric vehicle platforms is highly desirable. This paper is the first to two papers describing the development of a combustion system combining lean-stratified combustion with Miller cycle for downsized boosted applications. The work was completed under a multi-year US DOE project. The goal was to define a light-duty engine package capable of achieving a 35% fuel economy improvement at US Tier 3 emission standards over a naturally aspirated stoichiometric baseline vehicle. A multi-mode combustion system was developed enabling highly efficient lean-stratified operation at light-load and stoichiometric Miller-cycle operation at mid- to high-loads. Some of the unique challenges in synergistically combining the two concepts are highlighted in this paper. A central direct-injection four-valve layout was designed with high-tumble ports and a bowl-in-piston capable of stable operation under highly dilute (lean plus EGR) mixtures when properly matched to fuel spray characteristics, a multiple-injection strategy, and a high-energy ignition system. Other challenges specific to meeting exhaust emissions requirements with a high-efficiency engine are also explored in this study. In this Part 1 of the paper, the single-cylinder combustion system design, analysis, and testing is described. Multi-cylinder engine system design, analysis and development is described in Part 2 of the paper [<span class="xref">1</span>].</div></div>

Advancing Platooning with ADAS Control Integration and Assessment Test Results

<div class="section abstract"><div class="htmlview paragraph">The application of cooperative adaptive cruise control (CACC) to heavy-duty trucks known as truck platooning has shown fuel economy improvements over test track ideal driving conditions. However, there are limited test data available to assess the performance of CACC under real-world driving conditions. As part of the Cummins-led U.S. Department of Energy Funding Opportunity Announcement award project, truck platooning with CACC has been tested under real-world driving conditions and the results are presented in this paper. First, real-world driving conditions are characterized with the National Renewable Energy Laboratory’s Fleet DNA database to define the test factors. The key test factors impacting long-haul truck fuel economy were identified as terrain and highway traffic with and without advanced driver-assistance systems (ADAS). Track and on-highway testing guided by SAE J1321 procedures were conducted to assess truck platooning operation under the characterized real-world driving conditions. On-highway testing is done on a route in Indiana representing operation of long-haul Class 8 trucks in the United States. The road includes low-, medium-, and high-grade segments. The highway test results of a two-truck platooning configuration indicate considerable fuel-saving reduction comparing to the test track data collected under ideal driving conditions. The test data indicate that platooning could lead to increases in fuel consumption during traffic or high-grade portions of the route, causing reduction of the overall fuel saving on the road comparing to test track results. However, integration of ADAS features on the lead truck during on-road tests leads to significant improvement of fuel saving for both trucks in CACC operation.</div></div>

Electrified Deceleration Cylinder Cutoff Engine Control Benefits and Strategies

<div class="section abstract"><div class="htmlview paragraph">Cylinder deactivation is a fuel consumption and CO<sub>2</sub> reduction technology for internal combustion engines that deactivates cylinders at light to moderate loads, allowing the remaining firing cylinders to operate near optimum efficiency. Dynamic Skip Fire (DSF) uses full-authority cylinder deactivation that allows any cylinder of the engine to be deactivated in sequence. In previous SAE papers, both DSF technology and the synergies between DSF and electrification (eDSF) have been described.</div><div class="htmlview paragraph">Recent engine technology includes deactivation mechanisms that do not effectively incorporate individual cylinder control. Nevertheless, it is still quite possible to improve the efficiency of engines equipped with these ganged-deactivation mechanisms. By grouping all cylinders into a deactivation mode, no air is pumped through the cylinders as it would be during the corresponding conventional operation that deactivates fuel alone. The engine equipped with deactivating cylinders can eliminate pumping work and provide more opportunity to recuperate electrical energy from the vehicle, as well as prevent oxygen saturation of the exhaust aftertreatment system.</div><div class="htmlview paragraph">This paper presents the substantial benefits of this approach, showing both simulated and experimental fuel economy improvements for a C-segment vehicle exceeding 5% in the WLTC class 3 drive cycle. It discusses control strategies for managing transitions in and out of deactivation to achieve customer-pleasing NVH and drivability. Finally, the value proposition is presented for this electrified deceleration cylinder cutoff (eDCCO) system, also known as Hybrid++.</div></div>

Real-Time Integrated Power and Thermal Management of Connected HEVs Based on Hierarchical Model Predictive Control

Cabin heating requirement and engine efficiency degradation in cold weather lead to considerable increase in fuel consumption of hybrid electric vehicles (HEVs). This article focuses on the development of a real-time control framework for integrated power and thermal management (i-PTM) of a connected HEV through incorporating vehicle speed preview informed by traffic connectivity. To evaluate the ceiling of the fuel economy improvement via i-PTM, forward dynamic programming (FDP) is adopted as a benchmark study to find the global optimization solution and then implemented into a centralized model predictive controller (CMPC). Regarding the slow response associated with the coupled electric-thermal dynamics in the HEV, a two-layer hierarchical MPC (HMPC) framework is developed, which exploits vehicle speed preview predictions over short and long prediction horizons to optimize fuel consumption while satisfying power and cabin heating demand. The simulation results show that the proposed HMPC provides near-optimal solutions in reference to the benchmark while reduces up to 79% computation burden compared with CMPC. Additionally, compared to a baseline power management (PM) controller, up to 5.46% improvement on fuel economy can be achieved by HMPC for congested driving scenarios. Finally, the real-time feasibility of the HMPC is assessed in a dSPACE rapid prototyping system.

Deployment of fuel cell vehicles in China: Greenhouse gas emission reductions from converting the heavy-duty truck fleet from diesel and natural gas to hydrogen

Hydrogen fuel cells, as an energy source for heavy duty vehicles, are gaining attention as a potential carbon mitigation strategy. Here we calculate the greenhouse gas (GHG) emissions of the Chinese heavy-duty truck fleet under four hydrogen fuel cell heavy-duty truck penetration scenarios from 2020 through 2050. We introduce Aggressive, Moderate, Conservative and No Fuel Cell Vehicle (No FCV) scenarios. Under these four scenarios, the market share of heavy-duty trucks powered by fuel cells will reach 100%, 50%, 20% and 0%, respectively, in 2050. We go beyond previous studies which compared differences in GHG emissions from different hydrogen production pathways. We now combine an analysis of the carbon intensity of various hydrogen production pathways with predictions of the future hydrogen supply structure in China along with various penetration rates of heavy-duty fuel cell vehicles. We calculate the associated carbon intensity per vehicle kilometer travelled of the hydrogen used in heavy-duty trucks in each scenario, providing a practical application of our research. Our results indicate that if China relies only on fuel economy improvements, with the projected increase in vehicle miles travelled, the GHG emissions of the heavy-duty truck fleet will continue to increase and will remain almost unchanged after 2025. The Aggressive, Moderate and Conservative FCV Scenarios will achieve 63%, 30% and 12% reductions, respectively, in GHG emissions in 2050 from the heavy duty truck fleet compared to the No FCV Scenario. Additional reductions are possible if the current source of hydrogen from fossil fuels was displaced with increased use of hydrogen from water electrolysis using non-fossil generated electricity.

Impact of Active Control Turbocharging on the Fuel Economy and Emissions of a Light-Duty Reactivity Controlled Compression Ignition Engine: A Simulation Study

While forced induction strategies such as turbocharging can increase the power output and extend the load limit of engines operating on low temperature combustion strategies such as reactivity controlled compression ignition, the low exhaust enthalpy prevalent in these strategies requires the use of high backpressures to attain high turbocharger efficiencies, leading to high pumping losses and in turn poor fuel economy. Hence, there is a need to improve the exhaust energy utilization by the turbocharger such that the negative effects of the high backpressure requirements are offset. One turbocharger operating strategy that has the potential to enhance exhaust enthalpy conversion by the turbine is active control turbocharging (ACT), in which the rack position of a variable geometry turbocharger (VGT) is actuated using a continuously varying sinusoidal signal whose frequency is proportional to engine speed. In this study, the impact of ACT on turbocharger performance and fuel economy of a light-duty reactivity controlled compression ignition engine equipped with a VGT is investigated through coupled GT-POWER/KIVA-3V simulations at a medium-load cruise operating condition. A design of experiments study was executed in which the rack position amplitude and phase angle were independently varied, and the turbine efficiency, compressor efficiency, crankshaft torque, and brake specific fuel consumption were tracked for each run. The results show that ACT operation significantly increased the torque output while improving fuel economy over baseline VGT operation, but the range of actuation signal amplitude ratio was limited to 40% of the maximum amplitude possible due to peak cylinder pressure and peak pressure rise rate constraints. It is also shown that the impact of signal phase angle on turbocharger efficiency and overall system performance is not as significant as the amplitude ratio. The best fuel economy improvement over the baseline VGT operation at cruise conditions was observed at 40% amplitude ratio and 0° phase angle, and this value was 2.8%.

A Receding-Horizon Framework for Co-Optimizing the Velocity and Power-Split of Automated Plug-In Hybrid Electric Vehicles

Abstract This paper presents a control framework to co-optimize the velocity and power-split operation of a plug-in hybrid vehicle (PHEV) online in the presence of traffic constraints. The principal challenge in its online implementation lies in the conflict between the long control horizon required for global optimality and limits in available computational power. To resolve the conflict between the length of horizon and its computation complexity, we propose a receding-horizon strategy where co-states are used to approximate the future cost, helping to shorten the prediction horizon. In particular, we update the co-state using a nominal trajectory and the temporal-difference (TD) error based on co-state dynamics. Our simulation results demonstrate a 12% fuel economy improvement over the sequential/layered control strategy for a given driving scenario. Moreover, its real-time practicality is evidenced by a computation time per model predictive controller (MPC) step on average of around 80 ms within a 10 s prediction horizon.

Conventional automotive supply chains under China's dual-credit policy: fuel economy, production and coordination

As a sustainability policy in emerging markets, the dual-credit policy was implemented in China to reduce corporate average fuel consumption and to promote new energy vehicles (NEVs). Through a game theoretic approach, the fuel economy improvement level and the production of traditional internal combustion engine vehicles (ICEVs) and NEVs are discussed. Research and development cost sharing contracts and ICEV revenue sharing contracts are designed to coordinate conventional automotive supply chains. We compare the current and revised dual-credit policy, identify some policy flaws and propose amendments. The dual-credit policy does not always help automotive supply chains to improve fuel economy, reduce the production of high fuel consumption vehicles, and produce more low fuel consumption vehicles and NEVs. The implementation and selection of coordination contracts are explored. Both of the above contracts may not be able to coordinate the supply chain, and cost sharing contracts may be better than revenue sharing contracts in some cases. Finally, we present some management insights into the response to the dual-credit policy. • As a sustainability policy in emerging markets, dual-credit policy is explored. • Optimization models are established for conventional automotive supply chains. • Cost sharing contracts may be better than revenue sharing contracts. • Some policy flaws and amendments are proposed. • Novel management insights on specific action plans are generated.

Combustion optimization for fuel economy improvement of a dedicated range-extender engine

In this study, the combustion system of a dedicated range-extender engine was optimized based on a production engine for fuel economy improvement with the use of enhanced tumble flow, higher compression ratio, Atkinson cycle and exhaust gas recirculation (EGR). First, the shape of the intake port was optimized to improve in-cylinder tumble and turbulence for combustion enhancement. The computational fluid dynamics (CFD) results showed that compared to the original intake port, the peak tumble ratio during the compression stroke of the new port is improved by 74.0%, and the turbulent kinetic energy at the spark timing is increased by 33.0%, and the results were verified through the flow test bench experiment. The dyno experiment showed that, with the new intake port, the engine brake specific fuel consumption (BSFC) was improved for all test conditions. Then, the late intake valve closing (IVC) and a higher compression ratio were used in combination to adopt the Atkinson cycle. The IVC timing was set to 642° ATDC based on the preset power target. And the compression ratio was set to 12 to balance knock tendency and BSFC improvement. Finally, the cooled EGR was optimized to further suppress the knocking tendency to improve fuel consumption. The results showed that, with the cooling Strategy 2, the attainable maximum EGR ratio at 2400 rpm full load and 70 Nm conditions was increased, the spark timing could be significantly advanced, and the BSFC was improved. The improvement of BSFC is between 6 g/kW·h and 13 g/kW·h for the load range from 40 Nm to the full load. After the optimization, the minimum BSFC of the range-extender engine reaches 233 g/kW·h, while it is around 242 g/kW·h for the base engine. The operation area where fuel consumption is lower than 240 g/kW·h becomes much wider.

Design and Performance Analysis of a Cascaded Model Predictive Controller and Command Governor for Fuel-Efficient Control of Heavy-Duty Trucks

Abstract This paper presents the design and analysis of a predictive ecological control strategy for a heavy-duty truck that achieves substantial fuel savings while maintaining safe following distances in the presence of traffic. The hallmark of the proposed algorithm is the fusion of a long-horizon economic model predictive controller (MPC) for ecological driving with a command governor (CG) for safe vehicle following. The performance of the proposed control strategy was evaluated in simulation using a proprietary medium-fidelity Simulink model of a heavy-duty truck. Results show that the strategy yields substantial fuel economy improvements over a baseline, the extent of which are heavily dependent on the horizon length of the CG. The best fuel and vehicle-following performance are achieved when the CG horizon has a length of 20–40 s, reducing fuel consumption by 4–6% when compared to a Gipps car-following model.

Design Method for Reducing AC Resistance of Traction Motor Using High Fill Factor Coil to Improve the Fuel Economy of eBus

To improve electric vehicle (EV) mileage, motors must be designed for higher power density and efficiency. Winding technology with a high fill factor decreases the motor volume by reducing the unnecessary area of the slot. A motor conductor with a large cross-sectional area generates additional losses due to ac resistance, reducing the efficiency of the motor. Traction motors designed under these conditions can adversely affect EV fuel economy. Therefore, a design method is proposed to reduce the ac resistance of a traction motor by applying winding technology with a high fill factor to increase the fuel economy of the EVs. First, the characteristics and manufacturing process of the maximum slot occupation (MSO) coil, which is a winding technology with a high fill factor, are introduced. An ac resistance reduction method for a motor using an MSO coil is then proposed. Additionally, a motor designed using the proposed method is presented, and its electrical performance is compared with that of an initial motor. Next, the effect of the fuel economy improvement of the presently designed traction motor is verified through a vehicle simulation, and the validity of this article is experimentally confirmed using this motor.

An Improved Energy Management Strategy for Hybrid Electric Vehicles Integrating Multistates of Vehicle-Traffic Information

This study aims to answer the key question for hybrid electric vehicles (HEVs) on how to manage the power flow in HEVs with recent intelligent and electrified upgrades in automotive industries. The new energy management strategy (EMS) needs to fuse both the physic and cyber systems, reflecting the dynamic vehicle system in the physical layer, as well as taking full advantage of the outside information in the cyber layer. Given that, this article proposes the cyber-physical system (CPS)-based EMS using deep reinforcement learning (DRL) in two different types of vehicles [hybrid electric bus (HEB) and Prius]. Under the proposed framework, exploratory training is carried out for the EMS which is mediated by DRL algorithms, expert prior knowledge and multistate of traffic information. Then, the prior valid knowledge trained by HEB is applied to Prius based on deep transfer learning, accelerating the new EMS convergence and ensuring the same initial parameters of the two vehicles' deep neural networks. Moreover, the cyber information is decoupled from the vehicle itself, for the first time being visualized for comparative analysis. The results show a significant improvement by considering traffic states (TS) and using dynamic programming (DP) as a benchmark with 6.94% fuel economy improvement for deep deterministic policy gradients (DDPG) test results and 8.12% for deep Q-learning (DQL), respectively. The decoupling analysis distinguished the effect of various TS for the HEB and Prius due to their different characteristics in vehicle service, driving style, and vehicle structures, which further verifies the effectiveness of the proposed EMS.

Potential of CuS and CuO nanoparticles for friction reduction in piston ring–liner contact

ABSTRACT An experimental study on the use of Cu nanofluids as engine oils for reduction of friction in piston ring–liner contact is presented. The Cu nanofluids were prepared using CuS and CuO nanoparticles and blending them in 0.1–6% concentration by weight in the commercial engine oils. The Cu nanoparticles were synthesised and characterised after mixing with the lubricant for their physico-chemical characteristics. The experiments were performed on a reciprocating tribo-tester using actual piston rings and the liners as test specimens. Experiments were performed to investigate the effect of nanoparticle concentration with the change of contact loads and reciprocating frequency on the coefficient of friction. The study reveals that the Cu nanoparticles blended in the engine lubricating oils can significantly reduce friction between piston ring–liner contact, desirable for fuel economy improvement. The Cu nanoparticles are compatible with base lubricants and result in significant friction reduction. The CuS nanoparticles performed significantly better than the CuO nanoparticles.

Degradation adaptive energy management strategy using fuel cell state-of-health for fuel economy improvement of hybrid electric vehicle

Most studies on fuel cell hybrid electric vehicle energy management have focused on fuel economy. However, it is also important to consider the rapid degradation of the fuel cell. Therefore, a degradation-adaptive energy management strategy is proposed in this paper. The strategy can adaptively change the power distribution between different power sources using the fuel cell state-of-health. First, a novel degradation model is established for the fuel cell. The degradation model combines the polarisation curves of the fuel cell system under different state-of-health conditions and fuel cell efficiency models. An unbalanced degradation of the fuel cell at different current densities is shown in the degradation model. The proposed strategy is modified from an instantaneous optimisation energy management strategy by including state-of-health data. Accordingly, it is possible to provide optimised control based on the decrease in efficiency, thereby taking advantage of the unbalanced degradation. The proposed strategy can adaptively adjust the power distribution during degradation to get a higher energy efficiency over entire lifetime of fuel cell. The proposed strategy is adaptive to different degradation rates and consumes a small amount of computing resources, which ensure the feasibility of real-world implication. The performance of the proposed strategy is compared with that of the original strategy via simulation. The proposed strategy can optimise the fuel economy by 1.52–2.06% and 2.26–2.90% for a half and seriously degraded fuel cell, respectively. The results reveal that the proposed strategy provide an effective approach to improving the fuel economy of degraded fuel cell hybrid electric vehicles.

A review on Hybrid Electric Vehicle and simulation on Hybrid Electric Vehicle Drivetrain

This paper discusses about the Hybrid Electric Vehicle (HEV) and the simulation on the Parallel Hybrid Electric Vehicle Drivetrain. The concept and the classification of the HEV is discussed here and the simulation and analysis of a drivetrain for Parallel Electric Hybrid Engine (PHEV) was formulated on MATLAB Simulink. The methodology of simulation was to accelerate or decelerate the vehicle independently with the electric motor with engine running at constant speed. And the parameters such as electric motor and engine power were compared and observed. And research on CO2 emission and fuel consumption was made between the PHEV and Conventional Vehicle (ICE Vehicle). And the conclusion was made according to the research and the simulation that the development of HEV will have a great potential in fuel economy improvement.

Exploring the synergies of dynamic fuel management and propulsion electrification

Active fuel management is a technology that allows a multi-cylinder internal combustion engine to shut off a bank of cylinders to reduce engine power output as needed to match driving conditions and driver power demand. This variation in engine power is taken a step further with a technology known as dynamic fuel management, wherein individual cylinders of a multi-cylinder engine may be turned off as needed. This approach allows a finer resolution in engine power variation. Dynamic fuel management allows an engine to operate with lower pumping losses, thereby maximising the potential for capturing regenerative braking energy. In this paper, the synergies of such an engine equipped with dynamic fuel management, coupled to an electrified driveline incorporating a 48V P1 architecture, are investigated. Detailed MATLAB models are developed to understand the influence of system parameters as well as to identify the optimal engine/motor torque arbitration required for maximum fuel economy. It is shown that the combination of dynamic fuel management and P1 hybridisation can provide a substantial fuel economy improvement.

Maximizing Net Fuel Economy Improvement from Fusel Alcohol Blends in Gasoline Using Multivariate Optimization

Maximizing Net Fuel Economy Improvement from Fusel Alcohol Blends in Gasoline Using Multivariate Optimization

Exploring the synergies of dynamic fuel management and propulsion electrification

Active fuel management is a technology that allows a multi-cylinder internal combustion engine to shut off a bank of cylinders to reduce engine power output as needed to match driving conditions and driver power demand. This variation in engine power is taken a step further with a technology known as dynamic fuel management, wherein individual cylinders of a multi-cylinder engine may be turned off as needed. This approach allows a finer resolution in engine power variation. Dynamic fuel management allows an engine to operate with lower pumping losses, thereby maximising the potential for capturing regenerative braking energy. In this paper, the synergies of such an engine equipped with dynamic fuel management, coupled to an electrified driveline incorporating a 48V P1 architecture, are investigated. Detailed MATLAB models are developed to understand the influence of system parameters as well as to identify the optimal engine/motor torque arbitration required for maximum fuel economy. It is shown that the combination of dynamic fuel management and P1 hybridisation can provide a substantial fuel economy improvement.