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Increasingly stringent pollutant emission regulations and a customer demand for a high-fuel economy drive the modern automotive industry to hurriedly solve the problem of decarbonization and powertrain efficiency, leading R&D towards alternative powertrain solutions and fuels. Electrification, today, plays the biggest role in the topic, with Mild Hybrid Electrified Vehicles (MHEVs) being the most cost-effective architectures, displaying dominance in smaller markets such as Brazil. One of the biggest challenges for HEVs' development is the complexity of the hybrid control system, knowing when to actuate the electric machine, and the optimum power delivery, plus the gearshift schedule becomes a hard optimization problem that plays a key role in powertrain efficiency and cost savings for the customer. This paper proposes the implementation of a genetic algorithm (GA) as a machine learning-based control strategy to determine the torque split and the gear engaged for each driving condition of an MHEV operation, aiming to optimize fuel consumption. A quasi-static model of the vehicle was developed in Matlab/Simulink version 2022b, the virtual vehicle was then tested following the FTP75 and HWFET driving cycles. Simulation results indicate that the control decisions taken by the GA are qualitatively coherent for all operation conditions, and even quantitatively coherent in some cases, and that the software has the potential to be used as a control strategy outside the simulation environment, in future steps of development. [ABSTRACT FROM AUTHOR]

The transportation sector has the highest energy demand worldwide and bears the primary responsibility for CO2 emissions. Electromobility has emerged as the most feasible way to alleviate this problem. However, its potential depends heavily on the development of renewable energies. Island regions raise additional barriers to electromobility due to their heavy dependence on fossil fuels. This article addresses this challenge by presenting a comprehensive well-to-wheel framework to assess the levels of efficiency and CO2 emissions of electromobility options such as battery and plug-in electric vehicles (BEVs and PHEVs). The results were compared with those for internal combustion engine vehicles (ICEVs). The framework proposed takes account of various factors including the extraction, refining, and transport of oil, different segments of land vehicles, and electricity system operations. The framework is demonstrated with a case study of the Spanish Canary archipelago. The results show that BEVs improve efficiency and CO2 emissions by around 30% compared to ICEVs on islands where the share of renewable energies is higher than 21%. In contrast, limited renewable generation may lead to BEVs polluting up to 15% more than ICEVs. PHEVs should be considered as a suitable alternative if the share of renewable generation is higher than 35%. [ABSTRACT FROM AUTHOR]

Hydrogen electric locomotives are receiving growing attention and progressing towards net zero. This letter discusses the improvement of traditional diesel-electric locomotives to use hydrogen and reduce energy demand. An internal combustion engine featuring direct injection and jet ignition of the hydrogen is proposed. The four-strokes, 12 L, V12 engine achieves peak power of 750 kW and peak efficiency above 46%. The propulsion system may be further optimized by adopting battery energy storage to buffer the operation of the engine and provide extra power when accelerating. This way, the engine may be further optimized to work over a narrower range of speeds and loads and be made smaller to provide reduced power. • Conversion of diesel locomotives to hydrogen is straightforward. • Use of a larger traction battery help with energy recovery. • It also helps using a smaller engine to work more efficiently. • Fuel cell alternative has efficiency advantage, but weight, volume, power density, and TRL disadvantages. [ABSTRACT FROM AUTHOR]

Energy management in electrified vehicles is critical and directly impacts the global operating efficiency, durability, driveability, and safety of the vehicle powertrain. Given the multitude of components of these powertrains, the complexity of the proper control is significantly higher than the conventional internal combustion engine vehicle (ICEV). Hence, several control algorithms and numerical methods have been developed and implemented in order to optimize the operation of the hybrid powertrain while complying with the required boundary conditions. In this work, a model-based method is used for predicting the impacts of a set of possible control actions, choosing the one minimizing the associated costs. In particular, the energy management technique used in the present study is the equivalent consumption minimization strategy (ECMS). The novelty of this work consists of taking into account the thermal state of the ICE for optimization. This feature was implemented by means of an extensive experimental campaign at different coolant temperatures of the ICE to calibrate the additional fuel consumption due to operating the engine outside of its optimum temperature. The results showed significant gains in both WLTC and RDE cycles. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles are promising solutions to the need for cleaner transport. Their ability to drive fully electric also opens the possibility of zero local emission operation by turning off the internal combustion engine. However, prolonged periods with the engine turned off result in a cooldown of the aftertreatment system resulting in increased emissions when the engine is restarted. To remedy this problem, an emission management strategy that, based on pre-heating of the aftertreatment system, aims to reduce the impact of a prolonged engine-off event on NO x emissions is developed. The method works by locating each engine-off event and then handling each event separately using an optimization scheme that combines pre-heating and a causal heuristic emission management strategy. The individual events are linked using an equivalence factor that describes the decided trade-off between fuel and NO x . The equivalence factor can be chosen heuristically or iteratively to give the desired result in terms of NO x reduction and fuel consumption. The strategy is evaluated using simulations of a drayage drive cycle with multiple engine-off events. The results from the simulations show that for engine-off times below 0.5 h the strategy can reduce NO x compared to the baseline strategy while using the same amount of fuel. If the strategy is allowed more fuel, significant reductions in NO x can be seen for engine-off times up to 1.5 h, after which an exponential decay in the effectivity of the strategy is observed. It is also shown that the reduction in NO x is fairly linear in the equivalence factor, which gives the procedure of choosing it a predictable behavior. [ABSTRACT FROM AUTHOR]

The authors compare the energy consumption and CO2 emissions from vehicles using internal combustion engines (ICE), battery electric vehicles (BEV), fuel cell electric vehicles (FCEV), and two types of hybrid vehicles, BEV-ICE hybrid and BEV-FCEV hybrid. This paper considers several scenarios for four countries' electricity production from primary energy sources to estimate total CO2 release. Energy consumption of the vehicle per 100 km, emissions during manufacturing, battery production, and lifecycle of the vehicle are considered in the total amount evaluation of CO2 released. The results show that with current technologies for battery manufacturing, and a significant proportion of national grid electricity delivered by fossil fuels, BEV is the best choice to reduce carbon emissions for shorter driving ranges. In the case of electricity generation mainly by low-carbon sources, FCEV and BEV-FCEV hybrid vehicles end up with lower carbon dioxide emissions. In contrast, with electricity mainly generated from fossil fuels, electric vehicles do not reduce CO2 emissions compared to combustion cars. [ABSTRACT FROM AUTHOR]

The fuel consumption reduction increases the share of hybrid systems in the vehicle market. SUVs (sport utility vehicle) which is typically a kind of station wagon with off-road vehicle features are equipped with such systems increasingly more often. Using the representatives of this vehicle group (Toyota, Lexus), energy flow analysis was performed in the urban and extra-urban driving conditions in a typical agglomeration. The amount of energy used and recovered was estimated, taking regenerative braking into account. Analyzes were performed to determine the driving conditions in electric mode. The electric mode drive share of such vehicles in urban and extra-urban conditions was determined. There was a large share of electric mode during braking and an increased share when traveling at a constant speed. It has been shown that this driving mode share is significant at low and medium speeds, and decreases at a speed above 50 km/h (regardless of the driving conditions). The share of electric mode driving in urban conditions was estimated: it is at least 40 % of the covered distance and is significantly reduced in extra-urban driving conditions (share of up to 20 %). This article specifies the parameters of the electrical system cooperation in real driving conditions. Analyzes of these works are important to recognize the future possibilities of improving the control of the hybrid drive. [ABSTRACT FROM AUTHOR]

The combination of electric motors and internal combustion engines in hybrid electric vehicles (HEV) can considerably improve the fuel efficiency compared to conventional vehicles. In order to use its full potential, a predictive intelligent control system using information about impending driving situations has to be developed, to determine the optimal gear shifting strategy and the torque split between the combustion engine and the electric motor. To further increase fuel efficiency, the vehicle velocity can be used as an additional degree of freedom and the development of a predictive algorithm calculating good choices for all degrees of freedom over time is necessary. In this paper, an optimization-based algorithm for combined energy management and economic driving over a limited horizon is proposed. The results are compared with results from an offline calculation, which determine the overall fuel savings potential through the use of a discrete dynamic programming algorithm. [ABSTRACT FROM AUTHOR]

Rapid vehicle and powertrain development has become essential to for the design and implementation of vehicles that meet and exceed the fuel efficiency, cost, and performance targets expected by today’s consumer while keeping pace with reduced development cycle and more frequent product releases. Recently, advances in large-scale additive manufacturing have provided the means to bridge hardware-in-the-loop (HIL) experimentation and preproduction mule chassis evaluation. This paper details the accelerated development of a printed range-extended electric vehicle (REEV) by Oak Ridge National Laboratory, by paralleling hardware-in-the-loop development of the powertrain with rapid chassis prototyping using big area additive manufacturing (BAAM). BAAM’s ability to accelerate the mule vehicle development from computer-aided design to vehicle build is explored. The use of a hardware-in-the-loop laboratory is described as it is applied to the design of a range-extended electric powertrain to be installed in a printed prototype vehicle. The integration of the powertrain and the opportunities and challenges it presents are described in this work. A comparison of offline simulation, HIL and chassis rolls results is presented to validate the development process. Chassis dynamometer results for battery electric and range extender operation are analyzed to show the benefits of the architecture. [ABSTRACT FROM AUTHOR]

Hybridization of heavy off-highway working vehicles brings considerable energy savings in the form of a downsized internal combustion engine (ICE) by means of reduced no-load losses. In this paper, a novel energy saving opportunity in working hydraulics at the end of long booms of working vehicles is proposed. In traditional off-highway working vehicles, the working hydraulics is supplied through pipes, hoses, and valves by a hydraulic pump located near the main engine. A significant amount of energy is lost in long pipelines and hoses as well as in valve throttles. A new topology is introduced to supply the power along the long boom; the power for a hydraulic actuator is supplied by an integrated electro-hydraulic energy converter (IEHEC), which is located at the boom end. The electrical energy to the converter is supplied through electrical cables, which have negligible losses compared with a conventional fluid power supply with long pipelines. The converter transforms the electrical energy into hydraulic energy at the end of the boom, and may also recover energy for additional energy savings. [ABSTRACT FROM AUTHOR]

Despite the recent commercial success of hybrid, plug-in hybrid and electric vehicles their market share is still insufficient to produce either a significant impact on energy consumption on a global basis or a profitable automotive segment. In this context, the possibility of upgrading conventional vehicles to hybrid electric vehicles is gaining increasing interest. To this aim this paper investigated and modelled the intention to install an after-market hybridization solar-kit (HySolarKit) in order to ascertain the main behavioural determinants of the choice process and set up an operational model with which to estimate the market potential of such technology. In particular, two behavioural stages of the choice process were analysed and modelled: (i) the intention to adopt the HySolarKit; (ii) the choice to install the HySolarKit. Both issues were addressed through ad hoc stated preference surveys carried out in two different Italian cities, and through the specification and the calibration of discrete choice models based on the behavioural paradigm of random utility theory. Different modelling solutions (homoscedastic and heteroscedastic) were compared in terms of goodness-of-fit and sensitivity to level-of-service attributes. The results showed the technological potential of the HySolarKit, and that both behavioural stages may be effectively modelled through random utility theory. Estimation results allowed an interpretation of the main determinants of the investigated phenomena, making it possible to quantify the potential effects and the concerns towards such a green solution, and making it possible to draw up operative marketing strategies. In particular, the intention to adopt the kit mainly depends on socio-economic factors as well as activity-related and attitudinal attributes, whereas the probability of installing the kit is greatly affected, to the same extent, by installation cost, the charging cost and the weekly mileage driven. [ABSTRACT FROM AUTHOR]

This paper introduces a new low-cost hybrid drivetrain concept based on compressed air energy storage. In contrast to most contemporary approaches to pneumatic hybridization, which require modification to the primary power plant, this concept is based on a stand-alone pneumatic system that could be readily integrated with existing vehicles. The pneumatic system consists of an air tank and a compressor–expander that is coupled to the rest of the drivetrain via an infinitely variable transmission. Rather than incorporating more expensive technologies such as variable valve timing or a variable compression ratio compressor, a fixed valve system consisting of a rotary valve and passive check valves is optimized to operate efficiently over a range of tank pressures. The feasibility of this approach is established by thermodynamic modeling and the construction of a proof-of-concept prototype, which is also used to fine tune model parameters. While the proof-of-concept system shows a round trip efficiency of just under 10%, modeling shows that a round trip efficiency of 26% is possible with a revised design. If waste heat from the engine is used to maintain an elevated tank temperature, efficiencies of nearly 50% may be possible, indicating that the concept could be effective for practical hybridization of passenger vehicles. [ABSTRACT FROM AUTHOR]

While discrete choice analysis is prevalent in capturing consumer preferences and describing their choice behaviors in product design, the traditional choice modeling approach assumes that each individual makes independent decisions, without considering the social impact. However, empirical studies show that choice is social – influenced by many factors beyond engineering performance of a product and consumer attributes. To alleviate this limitation, we propose a new choice modeling framework to capture the dynamic influence from social networks on consumer adoption of new products. By introducing social influence attributes into a choice utility function, social network simulation is integrated with the traditional discrete choice analysis in a three-stage process. Our study shows the need for considering social impact in forecasting new product adoption. Using hybrid electric vehicles as an example, our work illustrates the procedure of social network construction, social influence evaluation, and choice model estimation based on data from the National Household Travel Survey. Our study also demonstrates several interesting findings on the dynamic nature of new technology adoption and how social networks may influence hybrid electric vehicle adoption. [ABSTRACT FROM AUTHOR]

Abstract: Vehicles powered with internal combustion engines (ICEs) are one of the main pollutant sources in large cities. Most of large cities (e.g. Amman, capital of Jordan) suffer from frequent traffic jams. This leads to frequent stops and starts, and hence, an increase in tailpipe emissions. One way to minimize emissions is to use electric motors in the powertrain configuration. In this study, the performance of a hybrid fuel cell (FC)/battery vehicle is investigated utilizing different worldwide driving cycles. Initially, a model of a mid-sized ICE vehicle is developed and validated against experimental tests. The ICE vehicle validated model is then modified to be driven with only an electric motor powered by a hybrid FC/battery system. The effect of driving pattern, which varies from city to city and from region to region, is investigated. A driving cycle that represents the driving patterns in Amman city is developed based on experimental data and then used to evaluate the performance of both ICE and hybrid FC/battery vehicle configurations. It is found that the performance of the hybrid FC/battery configuration is much better than the ICE version in terms of emissions, fuel economy, efficiency, and speed tracking error. [Copyright &y& Elsevier]

This paper presents a comparative study of induction machines (IMs) and interior permanent-magnet synchronous machines (IPMSMs) in traction applications. Machines are chosen to have similar ratings to operate in the same vector-controlled drive system and to achieve a fair comparison. Due to the dynamic nature of traction applications, static efficiency maps are not used. Instead, energy consumption is evaluated for both IMs and IPMSMs during typical drive cycles. Machine efficiency is also evaluated during the drive cycle to account for steady-state and transient conditions. The results of these comparisons show that the IPMSM can have higher efficiencies for most conditions. After comparing IMs and IPMSMs, loss minimization is integrated into the IM drive. Optimal flux commands are dynamically set, and then, energy and efficiency are compared with those of the IPMSM. Results show that IMs can achieve efficiencies comparable to the IPMSMs when loss minimization is applied. The efficiency gap between the IM and IPMSM is thus reduced, and these results are verified in both a hybrid electric vehicle simulator and a hardware-in-the-loop experimental setup. [ABSTRACT FROM AUTHOR]

Abstract: The energy management problem of finding the optimal split between the different sources of energy in a charge-sustaining parallel HEV, ensuring stability and optimality with respect to a performance objective (fuel consumption minimization over a driving cycle), is addressed in this paper. The paper develops a generic stability and optimality framework within which the energy management problem is cast in the form of a nonlinear optimal regulation (with disturbance rejection) problem and a control Lyapunov function is used to design the control law. Two theorems ensuring optimality and asymptotic stability of the energy management strategy are proposed and proved. The sufficient conditions for optimality and stability are used to derive an analytical expression for the control law as a function of the battery state of charge/state of energy and system parameters. The control law is implemented in a simplified backward vehicle simulator and its performance is evaluated against the global optimal solution obtained from dynamic programming. The strategy performs within 4% of the benchmark solution while guaranteeing optimality and stability for any driving cycle. [Copyright &y& Elsevier]

Comprehensive evaluation of greenhouse gas emission reductions from alternative vehicle powertrains requires the use of a life cycle assessment methodology, which considers manufacturing, utilization and end-of-life phases of the vehicle and fuel production. However, vehicle powertrain technologies and regional electricity and fuel production characteristics are evolving rapidly, making such studies performed using current data obsolete in few years. For example, regional electricity generation mixes across the Canadian provinces are largely different, and they are likely to evolve with the introduction of renewable electricity. Hence, there is a general need for adaptable and flexible life cycle assessment frameworks for the transportation sector for rapid evaluation of GHG reductions using up-to-date data. The objective of this study is to develop an adaptive life cycle assessment framework for light duty vehicles that can incorporate critical new developments in vehicle powertrain technologies and changes in electricity and fuel supply. Conventional, hybrid, plug-in hybrid, battery electric and fuel cell powertrain types are included in the framework as parametrized models. Critical parameters related to vehicle size and battery capacity, fuel consumption, mileage, battery chemistry, vehicle hybridization, and electricity and fuel emission intensity are incorporated. The framework was demonstrated by applying it in the Canadian context with the analysis of scenarios associated with electricity grid emission intensity, mileage, battery capacity and battery chemistry. [ABSTRACT FROM AUTHOR]

Energy consumption and the associated environmental impact are a pressing challenge faced by the transportation sector. Emerging electric-drive vehicles have shown promises for substantial reductions in petroleum use and vehicle emissions. Their success, however, has been hindered by the limitations of energy storage technologies. Existing in-vehicle lithium-ion battery systems are bulky, expensive, and unreliable. Energy storage system (ESS) design and optimization is essential for emerging transportation electrification. This paper presents an integrated ESS modeling, design, and optimization framework targeting emerging electric-drive vehicles. A large-scale ESS modeling solution is first presented, which considers major runtime and long-term battery effects, and uses fast frequency-domain analysis techniques for efficient and accurate characterization of large-scale ESS. The proposed design framework unifies design-time optimization and runtime control. This conducts statistical optimization for ESS cost and lifetime, which jointly considers the variances of ESS due to manufacture tolerance and heterogeneous driver-specific runtime usage. This optimizes ESS design by incorporating complementary energy storage technologies, e.g., lithium-ion batteries and ultracapacitors. Using physical measurements of battery manufacture variation and real-world user driving profiles, our experimental study has demonstrated that the proposed framework effectively explores the statistical design space and produces cost-efficient ESS solutions with statistical system lifetime guarantees. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a study of the economic and environmental balances for Electric Vehicles (EVs) versus Internal Combustion Engine Vehicle (ICEV). The analyses were based on the Well-to-Wheel (WTW) methodology, a specific type of Life Cycle Assessment (LCA). WTW balances were carried out taking into account different scenarios for the primary energy supply and different vehicle technologies. The primary energy supply includes non-renewable sources (fossil fuels and nuclear) and Renewable Energy Source (RES). Vehicle technologies include Battery Electric Vehicle (BEV), Hybrid Electric Vehicle (HEV) and Plug-in Hybrid Electric Vehicle (PHEV). The generation scenarios considered in the study include the present European Union (EU) average mix and a planned increasing contribution from RESs. For the BEV, several real world driving cycle scenarios were investigated, using a custom built data acquisition system, in order to characterize the main factors that contribute to the overall energy consumption, associated cost and emissions. In terms of environmental impact, for the average EU electricity mix, BEVs have less than a half of the emissions than an ICEV. However, the ownership costs during its life cycle (about 10y) are similar to an equivalent ICEV, despite the lower operational costs for BEVs. The likely battery price reduction, leading to a lower investment cost, will gradually tip the balance in favour of EVs. [Copyright &y& Elsevier]

Abstract: Energy consumption and exhaust emissions of hybrid vehicles strongly depend on the energy storage source and the applied control strategy. Heavy vehicles have the characteristics of frequent starts/stops and significant amounts of braking energy, which needs to find a more efficient way to store and use the high power flow. A novel parallel hybrid vehicles configuration consisting of hydraulic/electric synergy system is proposed to overcome the existing drawbacks of single energy storage source in heavy hybrid vehicles. A control strategy combining a logic threshold approach and key parameters optimization algorithm is developed to achieve acceptable vehicle performance while simultaneously maximizing engine fuel economy and maintaining the battery state of charge in its rational operation range at all times. The experimental and simulation results illustrate the potential of the proposed control strategy in terms of fuel economy and in keeping the deviations of SOC at high efficiency range. [ABSTRACT FROM AUTHOR]

Abstract: The paper focuses on investigating thermal-transients effects, associated to intermittent use of internal combustion engine (ICE), on fuel economy and hydrocarbon (HC) emissions of series hybrid solar vehicles (HSVs). An offline, non-linear constrained optimization is set-up to individuate the ICE power trajectory that simultaneously minimizes fuel consumption, suitably operates the battery and fully exploits daily solar contribution. The results highlight the importance of including thermal transients in HSV energy management. The combined effects of engine, generator and battery losses, along with cranking energy and thermal transients, produce non-trivial solutions for the engine/generator group, which should not necessarily operate at its maximum efficiency. [Copyright &y& Elsevier]

As hybrid electric vehicles (HEVs) are gaining more popularity in the market, the rule of the energy management system in the hybrid drivetrain is escalating. This paper classifies and extensively overviews the state-of-the-art control strategies for HEVs. The pros and cons of each approach are discussed. From different perspectives, real-time solutions are qualitatively compared. Finally, a couple of important issues that should be addressed in future development of control strategies are suggested. The benefits of this paper are the following: 1) laying down a foundation for future improvements, 2) establishing a basis for comparing available methods, and 3) helping devoted researchers choose the right track, while avoiding doing that which has already been done. [ABSTRACT FROM AUTHOR]

Abstract: The quest for more fuel-efficient vehicles is being driven by the increasing price of oil. Hybrid electric powertrains have established a presence in the marketplace primarily based on the promise of fuel savings through the use of an electric motor in place of the internal combustion engine during different stages of driving. However, these fuel savings associated with hybrid vehicle operation come at the tradeoff of a significantly increased initial vehicle cost due to the increased complexity of the powertrain. On the other hand, telematics-enabled vehicles may use a relatively cheap sensor network to develop information about the traffic environment in which they are operating, and subsequently adjust their drive cycle to improve fuel economy based on this information – thereby representing ‘intelligent’ use of existing powertrain technology to reduce fuel consumption. In this paper, hybrid and intelligent technologies using different amounts of traffic flow information are compared in terms of fuel economy over common urban drive cycles. In order to develop a fair comparison between the technologies, an optimal (for urban driving) hybrid vehicle that matches the performance characteristics of the baseline intelligent vehicle is used. The fuel economy of the optimal hybrid is found to have an average of 20% improvement relative to the baseline vehicle across three different urban drive cycles. Feedforward information about traffic flow supplied by telematics capability is then used to develop alternative driving cycles firstly under the assumption there are no constraints on the intelligent vehicle’s path, and then taking into account in the presence of ‘un-intelligent’ vehicles on the road. It is observed that with telematic capability, the fuel economy improvements equal that achievable with a hybrid configuration with as little as 7s traffic look-ahead capability, and can be as great as 33% improvement relative to the un-intelligent baseline drivetrain. As a final investigation, the two technologies are combined and the potential for using feedforward information from a sensor network with a hybrid drivetrain is discussed. [Copyright &y& Elsevier]

Abstract: This paper describes a modelling and dynamic control design process applicable to the development of fuel cell electric vehicles (FCEVs) and hybrid electric vehicles (HEVs). After an introduction to advanced propulsion technologies the development of FCEV at DaimlerChrysler is described, followed by a discussion on hydrogen as a fuel for FCEV and the challenges related to hydrogen storage. It is essential for advanced vehicles to obtain a range comparable to that of mass production vehicles sold today. Thus, there is a strong need to operate such vehicles with high efficiency and maximize the energy stored onboard a vehicle. A stochastic dynamic programming algorithm was developed and applied to the energy management of this FCEV, which allow fuel economy optimisation while keeping a good driveability. [Copyright &y& Elsevier]

Fuel consumption and emissions in parallel hybrid electric vehicles (HEVs) are directly linked to the way the load request to the wheels is managed between the internal combustion engine and the electric motor powered by the battery. A significant reduction in both consumption and emissions can be achieved by optimally controlling the power split on an entire driving mission (full horizon—FH). However, the entire driving path is often not predictable in real applications, hindering the fulfillment of the advantages gained through such an approach. An improvement can be achieved by exploiting more information available onboard, such as those derived from Advanced Driver Assistance Systems (ADAS) and vehicle connectivity (V2X). With this aim, the present work presents the design and verification, in a simulated environment, of an optimized controller for HEVs energy management, based on dynamic programming (DP) and receding horizon (RH) approaches. The control algorithm entails the partial knowledge of the driving mission, and its performance is assessed by evaluating fuel consumption related to a Worldwide harmonized Light vehicles Test Cycle (WLTC) under different control features (i.e., horizon length and update distance). The obtained results show a fuel consumption reduction comparable to that of the FH, with maximum drift from optimal consumption of less than 10%. [ABSTRACT FROM AUTHOR]

• Investigation on engine load control performed solely by fuel injection. • WOT operation with lean mixtures enabled with prechamber ignition systems. • Engine tests comparing load control by throttle and lambda variations. • Lambda load control proven effective, reducing pumping and heat losses. • Lambda load control at partial loads strongly reduces NO x , CO and CO 2 emissions. Energy shortages based on current global demand requires the technological solutions to increase fuel conversion efficiency in internal combustion engines. In this context, lean combustion is a relevant technique for increasing thermal efficiency and reducing pumping losses. Another strategy to reduce engine pumping losses is operating without restrictions imposed by the throttle valve, as in wide open throttle (WOT) conditions, when load is regulated by fuel injection. This technique, already used in diesel engines, is not currently used in Otto cycle engines due to limitations of operating outside fuel stoichiometry. However, lean operation is increasingly feasible with the development of technologies such as the prechamber ignition system (PCIS), which provides high ignition energy and mixture turbulence, improving combustion. The present work compares the effects of mixture enleanment in an Otto ethanol multicylinder engine using a prototype PCIS with the original ignition system running stoichiometrically. The results indicate that the use of PCIS enables the engine load control by variations of lambda, extending the lean limit to λ = 1.35 and λ = 1.6 with the homogeneous and stratified systems respectively. The use of PCIS in SI engines could increase the efficiency of stationary generator sets and hybrid vehicles, also reducing NO x , CO and CO 2 engine-out emissions at partial loads. [ABSTRACT FROM AUTHOR]

• COM-based models can control CA50 for different RCCI engine transient conditions. • Single-fuel RCCI operation with biofuels is a promising way to commercialize RCCI combustion. • Combustion mode-switching with COM-based models is required to capture transient RCCI emissions. • PHEVs with RCCI engine has the potential to reduce local and global emissions. Reactivity-controlled compression ignition (RCCI) is a promising energy conversion strategy to increase fuel efficiency and reduce nitrogen oxide (NOx) and soot emissions through improved in-cylinder combustion process. Considering the significant amount of conducted research and development on RCCI concept, the majority of the work has been performed under steady-state conditions. However, most thermal propulsion systems in transportation applications require operation under transient conditions. In the RCCI concept, it is crucial to investigate transient behavior over entire load conditions in order to minimize the engine-out emissions and meet new real driving emissions (RDE) legislation. This would help further close the gap between steady-state and transient operation in order to implement the RCCI concept into mass production. This work provides a comprehensive review of the performance and emissions analyses of the RCCI engines with the consideration of transient effects and vehicular applications. For this purpose, various simulation and experimental studies have been reviewed implementing different control strategies like control-oriented models particularly in dual-mode operating conditions. In addition, the application of the RCCI strategy in hybrid electric vehicle platforms using renewable fuels is also discussed. The discussion of the present review paper provides important insights for future research on the RCCI concept as a commercially viable energy conversion strategy for automotive applications. [ABSTRACT FROM AUTHOR]

• A fuel consumption model for Hybrid Electric Vehicles (HEVs) is developed. • Real driving tests of Argonne National Laboratory are used to verified the model. • HEVs show considerable fuel saving in various traffic conditions. • HEVs can not save much fuel when the traffic is in free flow state. Despite extensive work on the fuel consumption of vehicles, the evaluation of hybrid electric vehicles (HEVs) requires a model that can predict fuel consumption second by second. In this paper, a fuel consumption model for hybrid vehicles is developed based on the operation of internal combustion engine (ICE), electric machine (EM) and battery. The fuel consumption model for engine is based on steady-state fuel mapping with a transient correction. EM and battery module (EBM) is introduced to estimate the fuel consumption for different modes of hybrid vehicle. The Argonne National Laboratory (ANL) measurements of different driving cycles including the steady-state cycle, the Urban Dynamometer Driving Schedule (UDDS) cycle, the Highway cycle and the United States 06 (US06) cycle are used to verified the model. With cellular automaton simulation, we study the fuel consumption in traffic flow and show that the fuel consumption can be significantly reduced by hybrid mode. The mean fuel saving is approximately 30% in the traffic flow on a horizontal road. The fuel consumption of traffic flow under uphill, downhill, tailwind and headwind conditions also showes considerable fuel saving. The model can be used to evaluate the fuel economy of HEVs, and provide eco-driving route-choice suggestions for drivers. [ABSTRACT FROM AUTHOR]