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The accelerated adoption of Electric Vehicles (EVs) necessitates innovative and effective pricing strategies for charging infrastructure. This study leverages the MATSim (Multi-Agent Transport Simulation) framework to meticulously evaluate the performance of two distinctive EV charging pricing models: time-based charging and a hybrid model integrating both usage-based and time-based components. Driven by the pivotal question of how EV charging should be optimally priced—whether contingent on energy consumption or charging duration—the research endeavors to conduct two simulations. These simulations aim to provide a comprehensive comparative analysis, evaluating metrics such as total power derived from grids, utility revenue, charging station queues and served vehicles. In the time-based charging scenario, EVs incur charges based on their plugged-in duration, reflecting a pricing approach that correlates directly with the time a vehicle remains connected to the charging station. In contrast, in the hybrid model, EVs undergo initial billing based on usage until a predetermined battery charge point is reached, such as achieving a full battery. Subsequently, time-based pricing takes effect until the user disconnects the vehicle. The findings indicate that, in the combined approach, utility owners have the potential to generate more revenue. Conversely, the time-based approach demonstrates a capacity to serve a higher number of electric EVs, with comparable queue lengths observed in both approaches. Importantly, using the results of this paper, policymakers can suggest pricing schemes that maximize benefits for utility owners, reduce queues at charging stations, and ensure the security of power grids. [ABSTRACT FROM AUTHOR]

Despite massive investment and carbon-neutral transition goals set around the world, gasoline-based internal combustion engine vehicles still form an absolute majority in the transportation sector. With the advent of technology, access to electricity, uncertainties in fuel prices and health awareness, people worldwide are moving towards a better, reliable, cost-effective and environmentally friendly mode of transportation, a hybrid electric vehicle. Such a vehicle, with its powerful electric motor and compact gasoline-based engine, offers better efficiency in terms of operating cost and reliability. Considering the advent of hybrid electric vehicles taking pace, studies related to its architecture types, power flow dynamics, control and modelling of its various components will form an essential part of the automobile industry and research. This paper proposes a power flow topology for a hybrid electric vehicle with series-parallel architecture. The developed vehicle model with such an architecture type consists of three main sub-systems: the electrical system, the control system and the mechanical system. The presented power flow topology being modelled and analysed in detail in the Simulink tool is being implemented via a mode logic controller, which forms part of the central control system. The developed hybrid electric vehicle model demonstrates various modes of operation, from starting to accelerating to de-accelerating and then finally coming to a complete rest. Each mode yields and explains the following: the vehicle reference speed; the engine and generator turn functions on/off; the dc bus and battery voltage; the motor, battery, and generator current; the motor, generator, and engine speed; engine torque; engine power; throttle demand; and the vehicle's actual speed. The results thus obtained show that the waveforms associated with such topology, during its various modes of operation, are pretty stable and acceptable, thereby depicting and validating the operation of the developed hybrid electric vehicle model with proposed power flow topology in a precise and transparent manner. [ABSTRACT FROM AUTHOR]

The main contribution of this study is to introduce deep reinforcement learning (DRL) within the model prediction control (MPC) framework, and consider comprehensive economic objectives including fuel cell degradation costs, lithium battery aging costs, hydrogen consumption costs, etc. This approach successfully mitigated the inherent shortcomings of deep reinforcement learning, namely poor generalization and lack of adaptability, thereby significantly enhancing the robustness of economic driving decision in unknown scenarios. In this study, an MPC framework was developed for the energy management problem of fuel cell vehicles, and Bi-directional Long Short-Term Memory (Bi-LSTM) neural network was used to construct a vehicle speed predictor The accuracy of its prediction was verified through comparative analysis, and then it was regarded as a DRL model. Different from the overall strategy of the entire driving cycle, the model based DRL agent can learn the optimal action for each vehicle state. The simulation evaluated the impact of different predictors and prediction ranges on hydrogen economy, and verified the adaptability of the proposed strategy in different driving environments, the stability of battery state maintenance, and the advantages of delaying energy system degradation through comprehensive comparative analysis. • A health-conscious EMS framework based on deep reinforcement learning in MPC framework is proposed. • Fuel cell degradation model and lithium battery aging model are adopted to provide more perfect indicators for economic driving. • The advanced Bi-LSTM is used to construct the vehicle speed predictor in comparison with the benchmark LSTM. [ABSTRACT FROM AUTHOR]

In this study, fuel consumption map was created by obtaining a large number of data from gasoline engine passenger car through high-speed data acquisition with ISO-15765–4-CAN Protocol. Subsequently, CO 2 map was also generated theoretically. To obtain precise results, ANN was used while creating the maps. Later, conventional vehicle model was created with Matlab/Simulink. Afterwards, hybrid vehicle model was developed by implementing electrical system in compliance with single-shaft pre-transmission parallel configuration with one electric motor. Hybrid powertrain control algorithm was created by considering efficiencies of mechanical and electrical power sources and sinks. Charge-neutral simulations were run with conventional and hybrid models over WLTC and a local route which is created by collected in-city speed data. The hybrid and conventional vehicles were enabled to be compared with the developed method. Detailed comparison of ICE operations both between two vehicle models and two cycles was presented. Moreover, obtained results, including overall electrical system efficiencies, were compared over WLTC and the local route. Significant improvements both in consumption and CO 2 values were concluded. It is also concluded that using the hybrid system in local route with higher proportion of in-city driving conditions caused higher improvements despite a slight decrease in overall electrical system efficiency. [ABSTRACT FROM AUTHOR]

Current environmental concerns on nitrogen oxides (NO x) and particulate matter (PM) emissions caused by diesel engines have led researchers to be interested in investigating vehicles with alternative power sources. Because of this reason, vehicle models with SI engine were adopted in the conducted study. Firstly, as an initial step, 1-D SI engine models were created with use of AVL Boost software. A four-cylinder engine model was created for conventional vehicle model, while a two-cylinder downsized engine was adopted as a subsystem of hybrid vehicle model. The models were based on experimental data obtained from a laboratory test setup with a single-cylinder engine. Subsequently, detailed engine maps on emissions and fuel consumption were generated with the developed ANN model. The fuel consumption and emission data, which were gathered from NEDC and WLTC simulations, were compared for conventional ICE, PEM FC and PEM FC + ICE powered vehicles with the help of the vehicle model which was developed by using Matlab Simulink software. Based on the results, it was concluded that there might be sufficient improvement in fuel consumption and significant improvement in emissions with the use of PEM FC that a hybrid driving system (PEM FC + ICE) can be utilized, and that emissions can be at 0 with the sole use of PEM FC. • The PEMFC power source resulted in a significant reduction in fuel consumption. • The use of ICE-PEMFC provided slightly lower fuel consumption reduction than PEMFC. • FC reduction by ICE-PEMFC was higher under NEDC due to longer low power regions. • ICE-PEMFC model led to remarkable improvements in emissions especially in CO and HC. • NOx reduction by ICE-PEMFC was lower due to more efficient operations of ICE. [ABSTRACT FROM AUTHOR]

[Display omitted] • Novel predictive ECMS algorithm for hybrid trucks based on projected power demand. • Algorithm operation validated on commercial plug-in hybrid electric truck. • Up to 5.9% energy efficiency gains over a municipal operation profile. • Power-predictive ECMS for energy management brings real CO 2 benefits. This paper examines potential fuel consumption savings through the implementation of a predictive equivalent consumption minimisation strategy, as a control algorithm in the energy management system of hybrid heavy-duty vehicles. A plug-in hybrid electric truck simulation model was developed as a demonstrator and was validated with experimental data. The baseline vehicle model was based on the same algorithm as VECTO, which is further extended in the current study by novel factors that contain the average predicted power demand. The potential benefits of the proposed algorithm are examined for different heavy-duty categories over regulatory mission profiles in both charge-sustain and charge-depletion modes. The results show that the fuel consumption can be reduced by up to 5.9 % and 4.4% in charge sustaining and charge depleting modes, respectively, depending on the mission profile. If such an algorithm is adopted by commercial vehicles, this could lead to substantial CO 2 and fuel consumption savings on the road. [ABSTRACT FROM AUTHOR]

Energy flow analysis is one of the most effective methods for assessing and improving vehicle efficiency. It can provide the most effective support for the development and optimization of a hybrid electric vehicle (HEV) control strategy for improving the performance and energy saving of HEVs. In this study, an energy flow experiment of a series–parallel HEV is conducted in a climate chamber under the Worldwide Light-duty Test Cycle to address high fuel consumption. Then, GT-Suite and MATLAB/Simulink co-simulation models for the tested vehicle are built and calibrated. The problems of the original HEV control strategy are addressed using the analysis of vehicle energy flow distributions, operating conditions, and energy efficiency of key parts. Then, two optimized vehicle control strategies (termed Opt1 and Opt2) are employed to test vehicle fuel savings using a calibrated co-simulation model. The specific optimized vehicle control strategies primarily include canceling the series and parallel charging in the braking state, adding the engine direct-drive mode, and reducing the proportion of the parallel mode. The simulated results show that the 100 km fuel consumptions of Opt1 and Opt2 are reduced by 7.1% and 7.5%, respectively, compared with the original vehicle. The proportion of energy that drives the vehicle directly through the engines of Opt1 and Opt2 is increased from 45.7% to 70.2% and 61.6%, respectively. The total vehicle energy losses for Opt1 and Opt2 decrease from 29.0% to 22.5% and 22.9%, respectively. Although the engine of the original vehicle exhibits high thermal efficiency, the efficiency of the entire powertrain system is the lowest, resulting in the highest fuel consumption. [ABSTRACT FROM AUTHOR]

• Framework for modeling well to wheels real-world plug-in vehicles CO 2 using big data. • Simulations conducted using open-source code and public data sets. • Considered sensitivity to fuel mix in electric grid as well as charging behavior. • Examined several electric range designs for three plug-in powertrain architectures. • Too long electric range can be detrimental to equivalent CO 2 emissions. This paper presents a modeling and simulation study of the greenhouse gas (GHG) emissions of plug-in vehicles subject to real-world driving conditions. The simulations utilize open-source software tools for estimation of second-by-second energy and/or fuel consumption in more than 65 thousand real-world recorded trips in the California household travel survey (CHTS) data set. Statistical distributions of the equivalent well-to-wheels (W2W) GHG emissions corresponding to CHTS trips are generated for three baseline public-domain plug-in vehicle models of: (i) parallel-drive plug-in hybrid, (ii) serial-drive plug-in hybrid, and (iii) pure electric vehicle. In addition to the baseline models, several derived vehicle models are created by upsizing of the battery and powertrain via an iterative procedure to attain the new all-electric range (AER). The emissions model also considers days when a pure electric vehicle cannot meet the travel demand by assuming that the driving on those days is done on a conventional vehicle. For each of the considered powertrain architectures, optimizing the AER for minimum GHG emissions becomes a trade-off between reducing the gasoline driven miles and maintaining reasonably sized (lightweight) powertrain components. As such, the equivalent GHG emissions of a vehicle are shown to have diminishing returns in terms of AER, and the optimum AER depends on both the carbon content in the electric grid, as well as the charging behavior. Within considered modeling assumptions and several vehicle usage conditions, the optimum AER is estimated to be between 30 and 60 miles for PHEVs, and between 120 and 200 miles for BEVs. [ABSTRACT FROM AUTHOR]

• Propose a multi-period two-stage stochastic linear programming model. • A hybrid decomposition algorithm is developed to solve the mathematical model. • Impact of power demand uncertainty and flow rate on system performance are examined. • Several managerial insights are made based on our computational studies. This study proposes a novel formulation for designing and managing electric vehicle charging stations, considering both long-term planning decisions and short-term operational decisions over a pre-specified planning horizon and under stochastic power demand. To solve this challenging problem, we propose a hybrid algorithm that combines Sample Average Approximation with an enhanced Progressive Hedging Algorithm. A case study, based on the road network around Washington, D.C., is presented to visualize and validate the modeling results. Results indicate that the electric vehicle power demand are satisfied primarily via the grid and Vehicle-to-Grid when energy cost is low and solar power is unavailable. [ABSTRACT FROM AUTHOR]

• Empirically analyzes revealed adoption behavior of electric vehicles (EVs). • Distinguishes between hybrid EV (HEV), plug-in HEV (PHEV), and battery EV (BEV). • The latent green travel pattern construct negatively affects HEV and PHEV adoption. • Census tracts with more public EV charging stations encourage only PHEV adoption. • PHEV/BEV adopters live in non-apartment houses close to level 2 charging stations. This paper presents a behavioral model of public, revealed preferences (RP) for various types of electric vehicles (EVs) while accounting for a latent (green) travel pattern construct and charging infrastructure characteristics. Specifically, a two-level nested logit (NL) model is estimated to explain households' fuel type choice among five alternatives and three nests: (1) battery electric vehicles (BEVs); (2) hybrid vehicles including hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs); and (3) conventional vehicles including gasoline and diesel vehicles. Further, a latent travel pattern construct which captures a week-long number of trips by non-vehicle travel modes as well as daily vehicle and tollway use is estimated in a structural equation setting and subsequently fed into the NL model. Using a recent RP dataset from the California Household Travel Survey, we identify market segments for alternative fuel types based on households' socio-economic characteristics, built environment factors concerning public plug-in EV (PEV) charging infrastructure characteristics, latent and observable travel behavior factors of a household vehicle's principal driver, and vehicle attributes. The results highlight that the number of public PEV charging stations is only significant for households choosing PHEVs and interestingly insignificant in the BEV utility. Furthermore, the sensitivity analysis of the findings reveals that PHEV users are elastic with respect to household vehicle ownership ratio and the latent green travel pattern construct, while BEV users are inelastic to any explanatory variable. [ABSTRACT FROM AUTHOR]

• Engine and vehicle level simulations are performed to optimize dual-loop EGR system. • Based on original EGR conditions, extended EGR rates are conducted under engine level. • Dual-loop EGR split strategy is conducted for vehicle driving mode under WLTP. • Multi-objective Pareto optimization is performed to minimize NO X and BSFC. • Optimizing gear strategy, fuel economy and deNO X rate are improved to 1.5% and 5%. Because of the increased need for virtual analysis during the vehicle manufacturing processes, more stringent optimization methods are required for the simulation field. Owing to the use of big data from engine testing, 1D analysis can provide more powerful approaches in the conceptual design phase for car makers. In this study, system-level optimization of dual-loop EGR was performed at both the engine and vehicle level, whereas our previous research had been performed at the engine level. Depending on the virtually developed engine and vehicle models and control scheme from our previous works, improved driving capabilities could be observed under light-duty diesel vehicle systems under the world-harmonized light-vehicles test procedure (WLTP). The numerical model was extended through two steps. The first step includes model conversion from the base engine model with an HP EGR system to a virtual engine model with a dual-loop EGR system. The second step represents mode extension from the virtual engine model with a dual-loop EGR system to a vehicle model with a dual-loop EGR system. Optimizing the dominant parameters and using design of experiment (DoE)-based multi-objective Pareto optimization methods in each step, fuel economy could be improved by approximately, 1.5% and the deNO X rate was approximately 5% that of the conventional NEDC. It is implied that the dual-loop EGR system and gear strategy could improve vehicle performance under difficult driving conditions. [ABSTRACT FROM AUTHOR]

• Expert demonstration data of hybrid electric vehicle energy management are represented by domain adaptive meta-learning. • By embedding expert demonstration model, an apprenticeship-reinforcement learning framework is established to improve fuel economy. • It is verified under three hybrid vehicle models with different powertrain architectures and several driving cycles. • Compared to traditional reinforcement learning, the apprenticeship-reinforcement learning can effectively improve the fuel emergency and CO 2 emissions. Deep reinforcement learning (DRL) is a potential solution to develop efficient energy management strategies (EMS) for hybrid electric vehicles (HEV) that can adapt to the changing topology of electrified powertrains and the uncertainty of various driving scenarios. However, traditional DRL has many disadvantages, such as low efficiency and poor stability. This study proposes an apprenticeship-reinforcement learning (A-RL) framework based on expert demonstration (ED) model embedding to improve DRL. First, the demonstration data, calculated by dynamic programming (DP), were collected, and domain adaptive meta-learning (DAML) was used to train the ED model with the adaptive capability of working conditions. Then combined apprenticeship learning (AL) with DRL, and the ED model was used to guide the DRL to output action. The method was validated on three HEV models, and the results show that the training convergence rate increases significantly under the framework. The average increase that the apprenticeship-deep deterministic policy gradient (A-DDPG) based method applied to three HEVs achieved was 34.9 %. Apprenticeship-twin delayed twin delayed deep deterministic policy gradient (A-TD3) achieved 23 % acceleration in the power-split HEV. Because A-DDPG's EMS is more forward-looking and can mimic ED to some extent, the frequency of engine operation in the high-efficiency range has increased. Therefore, A-DDPG can improve the fuel economy of the series hybrid electric bus (HEB) by 0.2–2.7 %, and improvements averaged to about 9.6 % in the series–parallel HEV while maintaining the final SOC. This study aims to improve the sampling efficiency and optimal performance of EMS-based DRL and provide a basis for the design and development of vehicle energy saving and emission reduction. [ABSTRACT FROM AUTHOR]

—This paper proposes a real-time energy management strategy (EMS) for hybrid electric vehicles by incorporating reinforcement learning (RL) in a model predictive control (MPC) framework, which avoids the inherent drawbacks of RL—the excessive learning time and lack of adaptability—and remarkably enhances the real-time performance of MPC. First, the MPC framework for the energy management problem is formulated. In that, a novel long short-term memory (LSTM) neural network is utilized to construct the velocity predictor for a more accurate prediction, and its prediction capability is verified by a comparative analysis. Then, the HEV prediction model and the velocity predictor are regarded as the RL model with which the RL agent can interact. On this basis, the optimal control sequence in the prediction horizon can be learned through model-based RL, but only the first element is actually executed, and the RL process begins anew after the prediction horizon moves forward. In the simulation, the algorithm's convergence is analyzed and the influence of the prediction horizon length is evaluated. Then, the proposed EMS is compared with DP, conventional MPC, and RL method, the results of which demonstrate its performance and adaptability. As last, a hardware-in-the-loop test validates its actual applicability. • An energy management strategy is developed by incorporating RL in MPC framework. • The strategy avoids RL's inherent drawbacks and enhances MPC's real-time ability. • A long short-term memory neural network is utilized to predict the velocity. • Simulation and HIL test verify its fuel economy and real-time ability. [ABSTRACT FROM AUTHOR]

• A co-simulation platform for Hybrid Electric Vehicles is developed for virtual assessment of Energy Management, performance, CO 2 & pollutant emissions. • CO 2 & pollutant emissions models are based on convolutional neural network for accurate transient state prediction. • A continuous deep reinforcement learning based EMS of HEV is proposed. • DDQL is trained with random generated driving cycles based on Markov Chain approach from real recordings. • Cumulative d error for predicted pollutants emissions is below 8.5% while HEV simulation is running up to 10 times faster than real time in a standard laptop. The Energy Management Strategy (EMS) in an HEV is the key for improving fuel economy and simultaneously reducing pollutant emissions. This paper presents a methodology for developing hybrid models that enable EMS testing as well as the evaluation of fuel consumption, CO 2 and pollutant emissions (CO, NO x and THC). In this context, pollutant emissions are hard to quantify with static models such as the well-known map-based approach which is mainly due to the pronounced impact of transient effects. The novelty of this paper primarily comes from the characterization of pollutant emissions through Convolutional Neural Networks (CNN), providing high accuracy for both, instantaneous and cumulative values. The input parameters are classical Internal Combustion Engine (ICE) measurements such as engine speed, air mass flow, torque and exhaust temperature. The proposed CNNs are reduced to a minimum for low complexity and fast computability. These models are developed with experimental data from chassis dyno testing of a conventional turbo-charged gasoline engine vehicle. The pollutant emission models are used in conjunction with physical models of the remaining powertrain allowing for real time simulations of the complete HEV vehicle. The Double Deep-Q learning algorithm is proposed for the EMS and compared to the Dynamic programming (DP) solution. The introduced methodology is developed in a co-simulation framework between MATLAB-Simulink and AMESIM. The resulting model runs between 8 and 10 times faster than real time in an off-the-shelf PC. This provides the capability for developing models suitable for HIL (hardware-in-the-loop) and SIL (software-in-the-loop) applications. The final error in predicted CO 2 remains below 2.5% while the final cumulative error for pollutants is below 8.5% in the case of CO and HC emissions. [ABSTRACT FROM AUTHOR]

Energy management of Parallel Hybrid Electric Vehicles (PHEVs) involves computation of energy efficient torque-speed set-points for the engine and the motor. Application of Nonlinear Model Predictive Control (NMPC) for this problem has been reported in the recent literature. In this work, a fast iterative implementation of the NMPC, known as Model Predictive Static Programming (MPSP) is deployed for the first time for energy management of PHEVs, with an appropriate multi-objective cost function and the applicable equality and inequality constraints. Additionally, a systematic approach based on the Theory of Dominance is used to tune parameters of the controller efficiently. Performance is evaluated with a validated PHEV model on the ADVISOR simulation environment, in terms of various performance metrics on standard drive cycles. The results show that the performance of the MPSP-based technique is close to the ideal performance achieved with Dynamic Programming (DP) and improved over a standard implementation using the Linear Time-Varying MPC (LTV-MPC) and Sequential Quadratic Programming MPC (SQP-MPC) algorithm. Moreover, its execution time on an industry standard micro-controller board is seen to be significantly less than that of LTV-MPC and SQP-MPC. This algorithm therefore appears to be one of the best candidates for on-board vehicle implementation of an optimal energy management strategy for a PHEV. • A new improved energy management strategy for a Parallel HEV based on the MPSP framework. • Numerical implementation showing improved performance over a contemporary MPC algorithm. • Low execution time and feasibility of an on-board implementation. • A new effective approach for tuning the algorithm. [ABSTRACT FROM AUTHOR]

Vehicle stability and energy efficiency are important considerations in vehicle engineering. In this context, the current paper presents an energy saving strategy for hybrid electric vehicles that incorporates vehicle lateral dynamic control in conjunction with energy efficiency. To this end, we first model the nonlinear vehicle lateral dynamics of a hybrid electric bus via a Takagi–Sugeno approach and combine this model with an H_{\infty } state-feedback controller via parallel distributed compensation. The controller matrices are obtained using linear matrix inequalities through an optimal energy-to-energy performance norm of the nonlinear vehicle model. Second, we propose a reference side-slip angle generating method and a set of tire force distribution rules, which under the premise of ensuring vehicle stability, minimize the overall energy consumption of the vehicle. Finally, we put forward a new speed prediction method based on vehicle lateral dynamics for hybrid electric vehicle energy saving. Human-in-the-loop simulated driving experiments are conducted where the bus performs lane-changing maneuvers with enhanced control properties under various driving conditions, demonstrating the reliability of the proposed energy-saving performance measures. • We model the nonlinear vehicle lateral dynamics of a bus with the so-called Takagi-Sugeno fuzzy approach and combine with an H_{\infty } state-feedback controller. • We propose a novel vehicle power system layout, a reference side-slip angle generating method, and a set of tire force allocation algorithms. • A new speed prediction algorithm is introduced. • Human-in-the-loop experiment results show that the proposed method has enhanced control properties and reliable energy-saving performance. [ABSTRACT FROM AUTHOR]

Exergy, or availability, is a thermodynamic concept representing the useful work that can be extracted from a system evolving from a given state to a reference state. It is also a system metric, formulated from the first and the second law of thermodynamics, encompassing the interactions between subsystems and the resulting entropy generation. In this paper, an exergy-based analysis for ground vehicles is proposed. The study, a first to the authors' knowledge, defines a comprehensive vehicle and powertrain-level modeling framework to quantify exergy transfer and destruction phenomena for the vehicle's longitudinal dynamics and its energy storage and conversion devices (namely, electrochemical energy storage, electric motor, and ICE). To show the capabilities of the proposed model in quantifying, locating, and ranking the sources of exergy losses, two case studies based on an electric vehicle and a parallel hybrid electric vehicle are analyzed considering a real-world driving cycle. This modeling framework can serve as a tool for the future development of ground vehicles management strategies aimed at minimizing exergy losses rather than fuel consumption. • Development of an exergy-based modeling framework for ground vehicles. • All the energy storage/conversion devices are modeled relying on exergy principles. • Formal technique for location/quantification of vehicle powertrain irreversibility. [ABSTRACT FROM AUTHOR]

This paper presents the simulations and experiments for the development of a hybrid and reversible mobile air conditioning, organic Rankine cycle (ORC), and ejector refrigeration cycle (ERC) system. This flexible and compact system may be the solution to the introduction of waste heat recovery in passenger cars. The cycle fuel economy is calculated for series hybrid electric vehicles (SHEVs). A global vehicle model is used to simulate the annual weather conditions of different climatic regions. The reference driving cycle is the Worldwide Harmonized Light Vehicle Test Cycle. The cycle fuel economy is maximized in temperate and cold climatic regions. In the hypothesized engine cold-start initial condition, the SHEV ReverCycle maximum fuel economy is 1.2% in Paris. In the hot-start initial condition, the SHEV ReverCycle maximum fuel economy is 2.2% in Moscow. The experimental results of the ReverCycle proof of concept are then presented. The maximum measured net efficiency in the ORC mode is 3.9%. The ERC mode can satisfy cabin cooling requirements at an ambient temperature below 20 °C. The estimated fuel economy in the ERC mode is 1.4%. • ReverCycle is a low-cost and compact waste heat recovery solution for SHEV. • Experimental campaign on a reversible mobile AC/ORC/ejector refrigeration cycle. • ReverCycle shows a maximum measured net efficiency of 3.9% in the ORC mode. • Results of ejector testing show a potential fuel economy of 1.4% in ERC mode. [ABSTRACT FROM AUTHOR]

[Display omitted] • Assessment of low emission zone (LEZ) as potential decarbonisation instrument. • Econometric approach based on vehicle registration and real life fuel consumption data. • LEZ as effective measure for fostering alternative fuel vehicle uptake. • Support of hybrid and gas-powered vehicles impedes reaching CO 2 targets. • LEZ ineffective for reducing fleet CO 2 emissions; zero emission zone required. This study analyses the effect of a representative low-emission zone (LEZ) in Madrid on shifting vehicle registrations towards alternative fuel technologies and its effectiveness for reducing vehicle fleet CO 2 emissions. Vehicle registration data is combined with real life fuel consumption values on individual vehicle model level, and the impact of the LEZ is then determined via an econometric approach. The increase in alternative fuel vehicles (AFV) registration shares due to the LEZ is found to be significant but fosters rather fossil fuel powered AFV and plug-in hybrid electric vehicles (PHEV) than zero emission vehicles. This is reflected in the average CO 2 emissions of newly registered vehicles, which do not decrease significantly. In consequence, while the LEZ is a suitable measure for stimulating the shift towards low emission vehicles, a true zero emission zone would be required for effectively fostering also the decarbonization of the vehicle fleet. [ABSTRACT FROM AUTHOR]

• We perform comparative LCA of BEV, PHEV and ICEV in China. • Technology and energy foresight in 2030 are analyzed by different modeling methods. • BEV offers 23% of GWP mitigation in present and continuous reduction in 2030. • Electrification has great potentials for burden-shifting to toxicity impacts. • Assumptions and methodological choices have great effect on GWP up to 51%. In order to make the sustainable development of transport system, China has taken actions toward the electrification transition of vehicles. However, whether the electric vehicles (EVs) are more environmentally friendly than the conventional internal combustion engine vehicles (ICEVs) in China is still not clear due to a lack of complete and consistent environmental impact comparison of vehicles. This study takes the vehicle models from BYD Qin Pro series in China as illustrations to compare the environmental impact of battery electric vehicle (BEV) and plug-in hybrid electric vehicle (PHEV) with the conventional ICEV. The environmental profiles of vehicles across the whole life cycle within a set of environmental indicators are analyzed. The key aspects that may heavily influence the environmental impacts, i.e. regional heterogeneity, technology improvement and different modeling methods (attributional vs consequential) choices, are further tested, respectively. The results show that already today the BEV and PHEV powered by the current average Chinese electricity mix offer 23% and 17% reduction of global warming potential (GWP), respectively, compared to the gasoline ICEV. But it is achieved at the expense of considerable increases in mineral resource scarcity and ecological and human toxicity, etc. All of the factors considered show markedly influences on the environmental profiles of EVs, even up to 51% of GWP differences for different modeling methods application. And they all have the possibility to reverse the environmental priorities of some impact categories among ICEV, BEV and PHEV. [ABSTRACT FROM AUTHOR]

This paper considers two commercial and residential buildings for building energy resilience against natural disasters that cause a power outage. The buildings are modeled with a shared parking station for their electric vehicles. The peer-to-peer operation is modeled for the buildings. The electric vehicles inside the parking station have dissimilar patterns of availability and such dissimilarity helps the buildings to be benefited from the vehicles for extra hours. The power outage is modeled at different day hours and with various durations. The building is supported by energy management options to handle such disruptions. The options are a peer-to-peer operation of the building, electrical vehicle charging-discharging, partial charge ability, load curtailment, and load adjustment. The proposed model only utilizes available components of the buildings and it does not need to install further components. The purpose is to minimize energy cost and maximize energy resilience under natural disasters. The resilience is defined as critical load restoration and minimum energy loss under various power outages. The results demonstrate that the designated energy management options can practically minimize energy cost and improve energy resilience following blackouts. The electric vehicles can reduce energy cost by about 25% and supply the loads under 7-h power outage. • Building energy resilience against the natural disasters is studied. • Buildings are modeled with shared parking station for their electric vehicles. • Electrical vehicles are modeled with partial charge capability. • Load curtailment and adjustment options are included. • Energy resilience is the critical load restoration and minimum energy loss. [ABSTRACT FROM AUTHOR]

• PEV adoption can reduce greenhouse gas emission by up to 65% • Optimal PEV model assignment at household level is significantly different from vehicle level. • BEVs with 120+ miles range can benefit the most travel demand. • The achievable GHG emission reduction decreases by 2/3 if PHEV drivers never charge. There has been a continuous effort to quantify the expected greenhouse gas reductions from plug- in electric vehicles (PEVs). Most existing studies are based on standardized driving cycles to compare the performance of PEVs and conventional internal combustion engine (ICE) vehicles. This study is based on assigning the most efficient plug-in hybrid or full electric vehicle to a household based on the household travel behavior as collected by GPS tracking data of thousands of vehicles' real trips in California, and it found that PEV adoption can reduce greenhouse gas emission by up to 65%. We further explore the market share difference in assigning optimal vehicle models by considering vehicle-based and household-based travel demand, and we found that, given currently available technology, in many cases adoption of Plug-in Hybrid Electric vehicles (PHEVs) will not necessarily result in significantly higher greenhouse gas (GHG) emissions (2.8% higher) compared to battery electric vehicles (BEVs), while PHEVs have advantages to satisfy household's long-distance travel demand. However, if PHEV drivers never charge their vehicles, the achievable GHG emission reduction will be decreased by nearly 2/3. [ABSTRACT FROM AUTHOR]

• A location model is established based on existing activity patterns and time budgets. • The location model can minimize the trip loss of predetermined budget. • The vast majority of EV drivers' travel demand can be fulfilled. • Some drivers have to change activity patterns even in a larger charging network. • Missed-trip drivers are usually with longer travel distances and without home chargers. Deploying electric vehicle (EV) public charging stations could alleviate range anxiety of EV drivers, ensuring EVs to provide similar performances as internal combustion engine vehicles by maintaining their existing activities. This study develops a location model for EV public charging stations aiming to maximumly maintain the existing activities of EV drivers. First, a deterministic process of EV charging choice is proposed to simulate EV drivers' charging choice behavior. This process involves EV drivers' existing activities, home and public charging availability, range anxiety, and the energy consumption of remaining trips. Second, a coverage location model for EV public charging stations is proposed, aiming to maximize EV drivers' existing activities. Finally, the proposed model is applied to Beijing, China as a case study. The preliminary characteristics of drivers' trips are obtained, showing that the necessary energy for 46 % drivers on five weekdays exceeds the capacity of EV battery. The optimal solutions show that a decent amount of charging network could fulfill 90 % EV drivers' travel demand without changing their daily trips. The planned public charging stations mainly cover the high-tech and financial areas. The missed-trip EV drivers are also analyzed, being characterized with longer travel distances and without owning home chargers. [ABSTRACT FROM AUTHOR]

Online fault-diagnosis on system level for complex mechatronic systems takes multiple sensor measurements of the various components into account and contributes to a significantly increased system reliability by tracking down faults in the system at run time, enabling fault-specific recovery actions, such as reconfigurations. Ongoing efforts in the technological development of automobiles, especially in the field of driver assistance systems, yield more and more safety-critical systems, e.g., breaking control systems, and thus generate a high demand for reliable online diagnosis systems. In order to perform fault-diagnosis on system level, the interrelations between all measurements must be determined, which is a challenging and often demanding task done by human system experts. In this paper we present a systematic approach based on machine learning to establish online diagnosis for a hybrid-electric vehicle model in the context of the DAKODIS research project. With this paper we publish the Matlab/Simulink HEV research platform including a fault injection framework and data processing algorithms for active fault-diagnosis and recovery evaluations. [ABSTRACT FROM AUTHOR]

• Investigated a novel electric VRP considering uncertain environment. • Proposed a fuzzy optimization model with fuzzy simulation to the presented problem. • Integrated ALNS algorithm and VND algorithm to solve the presented model. • Performed experiments to verify the effectiveness of the proposed algorithm. As fuel prices increase and emission regulations become increasingly strict, electric vehicles have been used in various logistics distribution activities. Most studies have focused on the electric vehicle routing problem under a deterministic environment, neglecting the effects of uncertain factors in practical logistics distribution. Thus, a novel fuzzy electric vehicle routing problem with time windows and recharging stations (FEVRPTW) is investigated in this study, and a fuzzy optimization model is established based on credibility theory for this problem. In the presented model, fuzzy numbers are used to denote the uncertainties of service time, battery energy consumption, and travel time. Moreover, the partial recharge is allowed under the uncertain environment. To solve the model, an adaptive large neighborhood search (ALNS) algorithm enhanced with the fuzzy simulation method is proposed. In the proposed ALNS algorithm, four new removal algorithms are designed and integrated for addressing the FEVRPTW. To further improve the algorithmic performance, the variable neighborhood descent algorithm is embedded into the proposed ALNS algorithm and five local search operators are applied. The experiments were conducted to verify the effectiveness of the proposed ALNS algorithm for solving the presented model. [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]

Electric vehicles (EVs) are becoming the next automotive technology to be widely diffused in society. However, many structural challenges remain to be addressed around the EV socio-technical system as the technology progresses from a niche to early and late mass markets. This study investigates the challenges for electric vehicles focusing on their current and future business implications. For this purpose, we rely on a robust sample of 227 semi-structured interviews, conducted by the authors, with transportation and electricity experts from 201 institutions across seventeen cities in Denmark, Finland, Iceland, Norway, and Sweden. Our findings show that EVs currently face an unfavourable business case, led by the legacy of the petrol and diesel car industries, and national market conditions. This results in an unsuitable business model and supply chain that compromises EV production and market offerings. Additionally, we find that for wide diffusion in society, EVs will change the traditional automotive selling chain, directly affecting selling methods (i.e. dealerships), maintenance revenue streams and refuelling (recharging) structures. It is therefore essential to adopt new models, practices and methods of business that are suitable for EV diffusion. • Investigates the market case for electric mobility in the current automotive landscape. • It uses a sample of 227 semi-structure interviews across the five Nordic countries. • Identifies challenges on four main themes that create an unfavourable EV market case. • Identifies the need and potential of new business models tailored for. [ABSTRACT FROM AUTHOR]

The power-split hybrid vehicles are widely used owing to its advantages of high efficiency and low emission. With the increasing of the requirements of the customer's satisfaction nowadays, vibration control is getting progressively more attention. Since the transmission system components are elastic to some extent, meantime the torque of the motor responds quickly for a sudden acceleration, hence torsional vibration takes place, henceforth the passengers will suffer inconvenience attributable to this vibration. This paper studies the active control algorithm for the torsional vibration of the power-split hybrid powertrains under rapid acceleration and deceleration circumstances. Primarily, a dynamic modeling of the power-split hybrid vehicle is built, in addition the uncertainty caused by the variable transmission ratio is analyzed. Moreover, this paper proposes a method combining the model reference adaptive control and the pole configuration method to solve the torsional vibration active control problem with uncertainties. The reference model is constructed by pole configuration method. Based on the dynamic characteristics of the reference model, the model reference adaptive control is implemented and the torque ripple reduction of output shaft under the impact conditions is achieved. Furthermore, this paper designs a dynamic coordinated control algorithm combined with active vibration control strategy, which ensures the dynamic performance and comfort performance of the vehicle. Finally, the effectiveness of this active vibration control strategy under cyclic conditions is verified using Simulink simulation and hardware-in-loop simulation. • A dynamic model of hybrid vehicles that shows the uncertainty of system is proposed. • An active vibration control algorithm is proposed to active damping the vibration. • A dynamic coordinated control strategy is proposed to ensure the dynamic performance. [ABSTRACT FROM AUTHOR]