Diesel Hybrid Electric Vehicle Research Articles

Emissions from heavy-duty diesel, natural gas, and diesel-hybrid electric vehicles – Part 1. NOx, N2O and NH3 emissions

The current study characterized nitrogen oxide (NOx), nitrous oxide (N2O), and ammonia (NH3) emissions from 14 heavy-duty vehicles from different vocations (school bus, transit bus, refuse hauler, delivery vehicle, and goods movement vehicle) over different drive cycles using a chassis dynamometer. Test vehicles with engines ranging in model year from 2009 to 2018 and including diesel and compressed natural gas (CNG) vehicles. The diesel vehicles included both vehicles without selective catalytic reduction (no-SCR) and with SCR certified to the 0.2 g per brake horsepower-hour (g/bhp-hr) [0.27 g/kilowatt-hour (kW-hr)] NOx emissions standard that were operated with diesel fuel, with a subset tested on hydrotreated vegetable oil (HVO). The CNG vehicles were all three-way catalyst (TWC) equipped and certified to either the 0.2 or 0.02 g/bhp-hr NOx standard [0.27 or 0.027 g/kW-hr]. This study is part of a larger project that included over 200 in-use heavy-duty vehicles, which is one of the most extensive studies of emissions from modern heavy-duty vehicles to date. NOx, NH3, and N2O emissions varied depending on the vocation and the technology. Average NOx emissions for the Urban Dynamometer Driving Schedule (UDDS) cycle over all vehicles ranged from 0.003 to 6.16 g/bhp-hr [0.004 to 8.25 g/kW-hr] and from 0.02 to 17.2 g/mile [0.012 to 10.7 g/km], with the diesel vehicles showing the highest emissions, the 0.2 g/bhp-hr certified CNG vehicles showing lower emissions, and the 0.02 g/bhp-hr certified CNG vehicles showing very low emissions. NOx emissions over other different cycles were generally in the same range as those for the hot start UDDS, with the highway cycles generally showing the lowest emissions. CNG vehicles had relatively high NH3 emissions ranging from 0.13 to 0.34 g/bhp-hr [0.17 to 0.46 g/kW-hr] and from 0.44 to 0.97 g/mile [0.27 to 0.6 g/km] over the hot start UDDS compared to the SCR-equipped diesel vehicles due to NH3 formation across the TWC. N2O emissions did not show consistent trends and were near/at the detection limits for a number of the vehicles.

Fault mode detection of a hybrid electric vehicle by using support vector machine

Hybrid electric vehicle (HEV) is one of the ideal transportation tools to face the challenge of ‘Carbon peak carbon neutralization’. The high complexity of the latest HEVs result to the difficulties in vehicle fault diagnostics, which is considered to be the main factors of vehicle durability. In this study, a multi-fault mode detection method of a P2 diesel HEV was investigated by using support vector machine(SVM). The HEV physical model was built and validated by using the experimental data under China typical urban driving cycle (CTUDC). The SVM algorithm was coded in Matlab/Simulink environment. The training data vectors for normal mode and fault mode were acquired from the HEV model. Through the analysis of failure mode, the OVO-SVM method was proved as the best accuracy, the success rate of diagnosis reaches 98%. Case studies of single fault mode and multi-fault mode fault detection were conducted. Offline and online level test were performed to show the effectiveness of the detection algorithm. The study may support the development of intelligent diagnostic methods for the different types of HEVs.

Modeling and Integrated Optimization of Power Split and Exhaust Thermal Management on Diesel Hybrid Electric Vehicles

To simultaneously achieve high fuel efficiency and low emissions in a diesel hybrid electric vehicle (DHEV), it is necessary to optimize not only power split but also exhaust thermal management for emission aftertreatment systems. However, how to coordinate the power split and the exhaust thermal management to balance fuel economy improvement and emissions reduction remains a formidable challenge. In this paper, a hierarchical model predictive control (MPC) framework is proposed to coordinate the power split and the exhaust thermal management. The method consists of two parts: a fuel and thermal optimized controller (FTOC) combining the rule-based and the optimization-based methods for power split simultaneously considering fuel consumption and exhaust temperature, and a fuel post-injection thermal controller (FPTC) for exhaust thermal management with a separate fuel injection system added to the exhaust pipe. Additionally, preview information about the road grade is introduced to improve the power split by a fuel and thermal on slope forecast optimized controller (FTSFOC). Simulation results show that the hierarchical method (FTOC + FPTC) can reach the optimal exhaust temperature nearly 40 s earlier, and its total fuel consumption is also reduced by 8.9%, as compared to the sequential method under a world light test cycle (WLTC) driving cycle. Moreover, the total fuel consumption of the FTSFOC is reduced by 5.2%, as compared to the fuel and thermal on sensor-information optimized controller (FTSOC) working with real-time road grade information.

Real-world NOx emissions from heavy-duty diesel, natural gas, and diesel hybrid electric vehicles of different vocations on California roadways

This study assessed the real-world nitrogen oxide (NOx) emissions from 50 heavy-duty vehicles of different vocations and engine technologies using portable emissions measurement systems (PEMS). This is one of the most comprehensive in-use emissions studies conducted to date, which played a key role in the development of CARB's (California Air Recourses Board) updated EMission FACtor (EMFAC) model, especially for natural gas vehicles. In-use emissions testing was performed on school and transit buses, refuse haulers, goods movement vehicles, and delivery vehicles while were driven over their normal operating routes in the South Coast Air Basin. Engine technologies included diesel engines with and without selective catalytic reduction (SCR) systems, compressed natural gas (CNG) engines and liquified petroleum gas (LPG) engines, and SCR-equipped diesel hybrid electric vehicles. For most vehicles, the in-use NOx emissions were higher than the certification standards for the engine. Diesel vehicles generally showed higher brake-specific NOx emissions compared to the CNG vehicles. NOx emissions were strongly dependent on the SCR temperature, with SCR temperatures below 200 °C resulting in elevate brake-specific NOx. The 0.02 g/bhp-hr certified CNG vehicles showed the largest reductions in NOx emissions. The diesel hybrid electric vehicles showed important distance-specific NOx benefits compared to the conventional diesel vehicles, but higher emissions compared to the CNG and LPG vehicles. Overall, average NOx reductions were 75%, 94%, 65%, 79%, respectively, for the 0.2 CNG, 0.02 CNG, diesel hybrid electric, and LPG vehicles compared to diesel vehicles, due in part to some diesel vehicles with particularly high emissions, indicating that the widespread implementation of advanced technology and alternative fuel vehicles could provide important NOx reductions and a path for meeting air quality targets in California and elsewhere.

Cost-benefit analysis of alternative vehicles in the Philippines using immediate and distant future scenarios

Alternative vehicle technologies promise a sustainable future by reducing carbon emissions and pollution. However, their widespread adoption tends to be slow due to high costs and uncertainties in benefits. Using a life cycle-based approach, this study calculates ownership savings and societal benefits for various alternative vehicle technologies against their baseline vehicle technology (e.g. gasoline or diesel). The assessment is performed from a developing country context – in the Philippines. Furthermore, immediate and distant future scenarios are modeled. The immediate future scenario assesses costs and benefits if the shift is to happen now, while the distant future scenario considers the effect of widespread autonomous driving and ridesharing. The results of the study echo the significant societal benefits from electric- and fuel cell-powered vehicles found in literature, but they are hindered by high ownership costs. In the immediate future, the diesel hybrid electric vehicle can potentially have both positive societal and operational costs for public transportation. For a gasoline-powered private passenger car, a simple shift to diesel, 20% biodiesel or 85% methanol can be beneficial. In the distant future, it is expected that autonomous, rideshared vehicles can potentially lure people away from driving their own vehicles, because of lower costs per passenger-kilometer while sustaining the privacy and comfort of a private car.

Optimization Method for the Energy and Emissions Management of a Hybrid Electric Vehicle with an Exhaust Aftertreatment System

This paper presents a real-time optimization method to compute the fuel-optimal torque split, gear selection and engine on/off command for a Diesel hybrid electric vehicle equipped with an exhaust aftertreatment system. We aim to minimize the amount of fuel consumed, while achieving a charge-sustaining operation and keeping the tailpipe NOx emissions below the legislative limit. We simplify the full vehicle model to facilitate the formulation of a mixed-integer convex problem which is then solved using the proposed iterative convex optimization (ICO) algorithm. We validate the result by comparing it to the globally optimal solution computed using dynamic programming (DP). For the simple model, the ICO algorithm finds the same solution as the DP benchmark. The computation time was reduced from one week for the DP benchmark to 49s for the ICO solution. By comparing the DP solution obtained on the full model with the ICO solution evaluated on the full model, we observe an offset in the solution due to model mismatch, but find that the ICO algorithm captures the qualitative trends of the optimal solution. The proposed algorithm is capable of solving the energy and emissions management problem in real-time, forming the basis for online optimal control.

Research on Energy Management Strategy of Diesel Hybrid Electric Vehicle Based on Decision Tree CART Algorithm

Aiming at the emission of nitrogen oxides from hybrid electric vehicles (HEV), a strategy of diesel hybrid energy management based on decision tree CART algorithm was proposed.Firstly, a classification algorithm combining decision tree and regression tree is proposed to predict the trend change relationship of a case from one or more predictive variables according to the category and variable characteristics.Secondly, by controlling the torque distribution between the engine and the motor, an additional degree of freedom is introduced to adjust the trade-off between pure fuel economy and pure restriction.Finally, the simulation method is used to understand the system performance and adjust the proposed energy management strategy.The results show that in the proposed energy management strategy for diesel hybrid power, the reduction of NOx has an impact on fuel consumption, and the potential of NOx emission can be limited by choosing the best operating point and limiting engine power.

HİBRİD VE ELEKTRİKLİ ARAÇLARA YÖNELİK OTOPARK DÜZENLEMELERİ

While improvement works for internal combustion engines intended for decreasing exhaust gases which cause on-road vehicles based climate change are going on, the use of alternative fuel and alternative energy resources is on the increase. But there are certain criteria to negotiate for this kind of vehicles to be approved. These are the constraints such as; high initial investment cost for vehicles, limited fuel storage capacities and limited range. In addition to these, the problems such as safety and liability, high fuel filling costs, limited fuel/charging stations, infrastructure (network improvement) investments, developments in current trends mustn’t be ignored. In addition to conventional petrol and diesel engines, hybrid and full electric vehicle practices are becoming widespread in today’s cars. Another issue is that cars are parked during 90-95% of the day and parking functions require a number of structural changes. In this context, charging units and safe parking conditions are required to be provided in the parking lots in order to perform the charging operations of full electric vehicles (BEV), and especially rechargeable hybrids (PHEV, ReEV, EREV and RXBEV). In this study, the safety and functional integrity of external rechargeable hybrid and electric vehicles and charging units as well as parking lots will be examined.

Simulation of diesel hybrid electric vehicle containing hydrogen enriched CI engine

Hydrogen enrichment on diesel engines is a proven solution for both minimizing the undesirable emissions and fuel consumptions. Also, hybrid electric vehicles which manufactured for the same goal too, are playing an important movement during three decades in transportation sector. The combination of these two common-purpose technologies will give possibility to production of hybrid electric vehicles which have hydrogen-enriched internal combustion engine, in the near future.At this study, four type modelled vehicle; stock diesel vehicle (V1), hydrogen enrichment diesel vehicle (V2), hybrid electric vehicle which contains same diesel engine (V3) and hybrid electric vehicle that powered by hydrogen enrichment diesel engine (V4); simulated with AVL simulation tools for compared the performance and emission values, for the first time. V1 is outfitted by 3.0 L diesel engine. V2 is the hydrogen enriched version of V1 which hydrogen addition is conducted via intake manifold with 8% (vol/vol) enrichment. V1 and V2 were simulated under AVL Boost tool for analyzing the effects of hydrogen addition clearly. After that, V3 and V4 were modelled with AVL Cruise. V3 and V4 were coupled an electric motor (30 kW) with appropriate battery. In terms of performance and emissions results, vehicle types with hydrogen enriched diesel engines were given promising outputs when compared with without ones. In particular, V4 has revealing excellent performance. Under this study's circumstances, when compared V4 between stock one, 4.26% improvement was achieving on vehicle performance parameters. Additionally, the combined fuel consumption, NOx emission and CO2 emission decreases with 14.32%, 15% and 33% respectively, for comparison between V4 and V1.

Reduction of particulate emissions in diesel hybrid electric vehicles with a PMP-based control strategy

Over the last decades the pollutant emissions of passenger-car diesel engines have been significantly reduced by the emission standards, but it is known that in real driving conditions these vehicles significantly exceed the regulation limits. For this reason, an increasing number of cities and municipalities already are or are thinking about banning diesel cars to cope with local air pollution. This aspect together with the upcoming EURO 7 air pollution regulations are expected to push diesel cars out of the market, because of a too-much demanding requirement on the reduction in particulate and carbon dioxide emissions. The only way to make these cars keep a share in the automotive market is probably their hybridization. The aim of these paper is to propose a control strategy for the power split in diesel-hybrid vehicles, aimed at minimizing particulate emissions and controlling the battery state of charge simultaneously. To this aim, a supervisory controller, based on Pontryagins Minimum Principle, is designed for on-line energy management optimization of a Diesel Plug-in Hybrid Electric Vehicle with a parallel architecture. The proposed strategy focuses on the reduction of particulate emissions that represents a major issue to meet emissions standards in such vehicles. In this study a methodology based on software in the loop simulations is applied in order to properly tune the proposed energy management strategy and to understand the overall powertrain performance. This work demonstrates the potential of using the hybrid architecture to limit particulate emissions even under real-world-like driving cycles.

Optimal supervisory control of a Diesel HEV taking into account both DOC and SCR efficiencies

Car builders are facing restrictions on both fuel consumption (FC) and pollutant emissions. In this context, Diesel Hybrid Electric Vehicles (HEV) are an interesting solution since they are more fuel-efficient than their gasoline-powered counterparts. However, Diesel pollutants remain a concern as, despite the fact that particulate emissions are treated by the Diesel Particulate Filter (DPF), tailpipe NOx emissions are still problematic. Previous works have shown that a better FC to pollutants trade-off can be achieved if the Energy and Emissions Management Strategy (EEMS) of the HEV includes the pollutants downstream an After-Treatment System (ATS). For Diesel HEV, a previous approach which took into account the Selective Catalytic Reduction (SCR) has been applied. This paper aims to find a better trade-off by adding the Diesel Oxidation Catalyst’s (DOC) influence on the SCR’s efficiency. Consequently, three EEMS taking into account more or fewer ATS are calculated with the Dynamic Programming (DP) algorithm on the Worldwide Light vehicle Test Cycle (WLTC). Hence, each strategy is applied to the full energy and emission model in order to deduce a fuel consumption to tailpipe NOx tradeoff. The tradeoffs for each strategy are compared. As a result, all the strategies can reduce NOx emissions by 30% with a FC penalty of 3%. In addition, the three EEMS have shown similar FC/tailpipe NOx trade-offs but the EEMS that took ATS into account were able to produce 3% more engine-out NOx than the strategy with no ATS. To ensure the optimality of the solutions, the impact of the states’ discretization in the DP algorithm is investigated. Despite a high discretization level for each state, the SCR’s temperature variation is still over-approximated and might underestimate the benefit of a strategy considering the complete Diesel ATS.

Well-to-wheel assessment for informing transition strategies to low-carbon fuel-vehicles in developing countries dependent on fuel imports: A case-study of road transport in Lebanon

Road transportation worldwide is undergoing a rapid transition to more sustainable alternative fuel vehicle technologies as an effective means of dealing with climate change and related challenges. Several well-to-wheel studies have been done in mostly industrialized countries to assess the environmental impacts of these technologies as compared to conventional fuel vehicles. This study is a well-to-wheel assessment for the case of Lebanon and similar fuel-importing countries in the developing world where energy and transportation infrastructure are typically underdeveloped. The study considers the energy use, GHG and criteria pollutant emissions and economic costs for conventional and potentially feasible alternative fuel vehicle pathways for the Lebanese case in order to inform transition strategies to alternative fuels over the near, medium and long-terms. Results show that electric vehicles are beneficial for the long term as they require costly charging infrastructure and a clean electricity mix. Plug-in hybrid electric vehicles are attractive for the medium term, with gasoline or diesel hybrid electric vehicles the most feasible and beneficial technologies in the short-term. A sensitivity analysis showed that natural gas-based vehicles are competitive at high driving mileage, while locally produced biodiesel from waste cooking oil proved beneficial if emission controls are enforced.

Portfolio analysis of alternative fuel vehicles considering technological advancement, energy security and policy

In recent years, there has been an increasing interest in alternative fuel vehicles (AFVs), such as electric vehicles (EVs), fuel cell vehicles (FCVs) and compressed natural gas (CNG) vehicles, as a promising option for mitigating global warming and reducing energy consumption. Most studies in this area have been conducted on only a few types of powertrains, e.g. EVs and gasoline vehicles; to fill this gap, this study will cover FCVs, CNGs, hybrid electric vehicles, diesel hybrid electric vehicles and liquefied petroleum gas (LPG) vehicles. This research is novel because it includes energy security aspects, uses scenario analysis and investigates FCVs. This study aims to predict an optimal AFV portfolio based on different scenarios to sustain energy security in light of gas and petroleum restrictions until 2030. To do this, we will present four scenarios that consider improvements in technology, energy security requirements, decreasing petroleum prices and government subsidies. The Polish market is considered as a case for demonstrating the optimal model. The results indicate that it is crucial to introduce all types of powertrains to achieve both economic and energy security objectives. The projected diffusion of FCVs will be more pronounced than that in previous studies, owing to the expected rapid decline in the cost of both infrastructure and purchase price of cars. Furthermore, the projected deployment of AFVs in transportation systems in this study will mostly occur in the form of lorries (trucks) and passenger vehicles. Because CNG vehicles are expected to achieve a high degree of diffusion in the transportation system, the government should seek a reliable CNG supply. Overall, this research will help automakers and policymakers recognise investment possibilities.

Can propulsion and fuel diversity for the bus fleet achieve the win–win strategy of energy conservation and environmental protection?

China is facing serious issues involving energy sufficiency, greenhouse gas (GHG) emissions and air pollution caused partly by the rapid growth of vehicles. In order to relieve those problems, new energy vehicles are introduced into the bus and car market. We adopt life cycle analysis to evaluate the well-to-wheels (WTW) energy consumption, CO2 emissions and pollutant emissions from the traditional diesel bus and new energy buses, including diesel hybrid electric vehicles (HEVs), compressed natural gas vehicles (CNGVs) and battery electric vehicles (BEVs). This study reports the current situation and projects future scenarios for the BEV bus for several regions in China due to significant regional differences in the power generation mix. Compared to the diesel bus, the HEV bus can reduce petroleum, fossil fuel use and CO2 emissions by about 20%, and, at the same time, produce stable reduction benefits for all air pollutants. The CNG bus achieves reductions of WTW primary PM2.5 emissions by 70% over its diesel counterpart and, of course, uses little petroleum; but increases fossil fuel consumption moderately and has no benefit in GHG emissions. The BEV bus can deliver a substantial petroleum consumption advantage and greatly reduce the WTW NOX, VOC and CO emissions; but, if the electricity is generated from burning coal, the BEV bus has no PM2.5 emission benefit compared to the conventional diesel bus. Currently, the BEV bus increases fossil energy use and CO2 emissions in the coal-dominated regions; but, in the future, it can achieve substantially lower fossil energy use and CO2 emissions with more penetration of clean electric energy. To reach the win–win strategy, a city’s initial reliance on diesel buses for the public fleet has to give way to a mixture of these new energy buses; and the fleet mix should be diversified over the region and modified over time to accommodate changes in these energy and environmental parameters.

Hardware-in-the-loop validation of a power management strategy for hybrid powertrains

Previously, a hybrid powertrain management strategy was developed that controls the power sources based on frequency content, mitigating aggressive engine transients. This article presents a hardware-in-the-loop validation of this strategy, with a real engine and battery integrated into a diesel hybrid electric vehicle simulation, thereby allowing for a realistic evaluation of fuel economy, soot emissions, and battery life. Considering an aggressive drive cycle and a state-of-charge regulation strategy as a benchmark, the frequency-based strategy yields 5.9% increase in fuel economy, 62.7% decrease in soot emissions, and 23% reduction in effective Amp-hours processed, which should yield an increase in battery life.

Equivalent Consumption Minimization Strategy for the Control of Real Driving NOx Emissions of a Diesel Hybrid Electric Vehicle

Motivated by the fact that the real driving NOx emissions (RDE) of conventional diesel vehicles can exceed the legislation norms by far, a concept for the control of RDE with a diesel parallel hybrid electric vehicle (HEV) is proposed. By extending the well-known equivalent consumption minimization strategy (ECMS), the power split degree of freedom is used to control the NOx emissions and the battery state of charge (SOC) simultaneously. Through an appropriate formulation of the problem, the feedback control is shown to be separable into two dependent PI controllers. By hardware-in-the-loop (HIL) experiments, as well as by simulations, the proposed method is shown to minimize the fuel consumption while tracking a given reference trajectory for both the NOx emissions and the battery SOC.

Energy Management Strategies for Diesel Hybrid Electric Vehicle

This paper focuses on hybrid energy management for a Diesel Hybrid Electric Vehicle (HEV) with a parallel architecture. The proposed strategy focuses on the reduction of Nitric Oxides (NOx ) emissions that represents a key issue to meet diesel emissions standards. The strategy is split in two separated functions aiming at limiting the NOx in steady-state and transient operating conditions. The first functions, control the torque split between the engine and the electric motor. This energy management is based on the Equivalent Consumption Minimization Strategy (ECMS) where an additional degree of freedom is introduced to tune the optimization tradeoffs from the pure fuel economy case to the pure NOx limitation case. The second function adapts the torque split ratio between the motor and the engine, initially computed from the optimal control strategy during transient operations where NOx are produced. The engine torque correction relies on mean value models for the EGR system dynamics and for the NOx formation. This paper applies a methodology based on Software in the Loop (SiL) and Hardware in the Loop (HiL) simulations in order to understand the system performance according to the powertrain configurations and also to tune the proposed energy management strategy. The simulation results are confirmed by experiments performed on Hybrid-Hardware in the Loop (Hy-HiL) test bench. This work shows the potential of using the hybrid architecture to limit NOx emissions by choosing the best operating point and by limiting the engine dynamics. The NOx reduction has limited impact on fuel consumption.

Optimal energy management for a diesel hybrid electric vehicle considering transient PM and quasi-static NOx emissions

In this paper, optimal energy management strategies are derived to balance fuel consumption, raw particulate matter (PM) emissions, and raw nitrogen oxide (NOx) emissions for a Diesel hybrid electric vehicle. Two methods for the derivation of these strategies are compared. One method is based on dynamic programming and steady-state engine maps only. The second method is based on dynamic programming, steady-state engine maps, and a validated transient PM emission model. As a result, only the second method allows for the generation of smooth engine set point trajectories to reduce transient PM emissions without compromising fuel consumption and NOx emissions.

Architectural study of diesel hybrid propulsion systems to meet future fuel economy and emission regulations

Among conventional propulsion systems, the diesel propulsion system is very efficient in terms of fuel economy. However, conventional diesel propulsion systems have to meet aggressive fuel economy and stringent emission regulations. Electrification of the diesel propulsion system can play a major role in achieving future fuel economy and emission standards. In this paper, diesel hybrid propulsion system architectures are presented that could potentially meet future fuel economy and emissions compliance standards for a given set of performance criteria. Three diesel hybrid electric vehicle (D-HEV) architectures; parallel hybrid (PH), power split (PS) and series hybrid (SH) are investigated and analyses of these architectures are conducted. These architectures are compared and evaluated considering trade-offs between fuel economy and emissions targets.

연비 평가를 위한 6속 DCT기반 HEV 성능 시뮬레이터의 개발

With aggravation of environmental contamination and energy resource exhaustion, Hybrid Electric Vehicles (HEV) that can be economically operated with low fuel consumption are receiving greater attention. For performance improvement of such HEV, the development of efficient transmission can be seen as one of core technologies such as performance of components and driving strategy. Dual clutch transmission (DCT) is actively studied as a transmission type for HEV due to its advantages of having excellent power transmission efficiency based on manual transmission characteristic, resolving the problem of power interruption, and realizing driving convenience of automatic transmission (AT). In this paper, one diesel HEV equipped with 6-Speed DCT, modelled using MATLAB/Simulink, and a performance simulator developed for this vehicle are introduced. Driving simulation with driving cycles such as FTP75 and NYCC was performed using the developed performance simulator, and the simulated results regarding state of charge and fuel economy, when AT and DCT are applied to this diesel hybrid vehicle respectively, are compared. This performance simulator can be utilized to develop a control algorithm for improving the fuel economy of HEV with DCT.