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[en] Highlights: • We conduct a natural field experiment on incentives for fuel-efficient driving. • A monetary and a tangible non-monetary reward for eco-driving are compared. • The non-monetary reward results in an average reduction of fuel consumption of 5%. • There is only a small reduction effect in the equivalent monetary reward treatment. • Emphasis of fun, emotional responses and frequency of recalling might play a role. - Abstract: Reducing greenhouse gas emissions is a highly prevalent goal of public policy in many countries around the world. Convincing people to drive more fuel-efficiently (“eco-driving”) can contribute substantially to this goal and is often an integral part of policy initiatives. However, there is a lack of scientific studies on the effects of individual monetary and non-monetary incentives for eco-driving, especially in organizational settings and with regards to demonstrating causality, e.g., by using controlled experiments. We address this gap with a six months long controlled natural field experiment and introduce a monetary and a non-monetary reward for eco-driving to drivers of light commercial vehicles in different branches of a logistics company. Our results show an average reduction of fuel consumption of 5% due to a tangible non-monetary reward and suggest only a small reduction of the average fuel consumption in the equivalent monetary reward treatment. We find indications that more emphasis on the fun of achieving a higher fuel efficiency, a more emotional response to non-monetary incentives, and a higher frequency of thinking and talking about non-monetary incentives might play a role in the stronger effect of the tangible non-monetary reward. Policy implications for private and public actors are discussed.
[en] Highlights: • Different PHEV powertrain configurations are comparative analyzed. • Configuration-sizing-control integrated multi-objective optimization is developed. • PHEV powertrain configuration evaluation methodology is proposed. • Pareto optimal selection of PHEV powertrain configuration is provide. - Abstract: This study provides an optimal selection methodology for plug-in hybrid electric vehicle (PHEV) powertrain configuration by means of optimization and comprehensive evaluation of powertrain design schemes. The challenge of this study is to reveal each powertrain configuration performance potential in different situations of object trade-off and solve the control-physical integrated optimization problem of the PHEV powertrain design. To determine performance potential, a configuration-sizing-control strategy integrated multi-objective powertrain optimization design is proposed and applied to series, parallel pre-transmission (P2), output power-split, and multi-mode power-split powertrain configurations. Firstly, considering simultaneous optimization of fuel economy, electric energy consumption, and acceleration capacity, the parameters of the powertrain components and vehicle performance of each configuration are optimized based on global optimal control in different situations of object trade-off. Then, the Pareto optimal selection of powertrain configuration and its corresponding optimal component parameters are obtained by performance comparison and non-domination sorting. The results suggest that the P2 configuration and its optimal sizing can be selected when the goal is to optimize acceleration capacity, the multi-mode power-split configuration and its optimal sizing can be selected when the goal is to optimize electric energy efficiency, and the output power-split configuration and its optimal sizing can be selected when the fuel economy needs to be optimized.
[en] Highlights: • A new anti-idling system for refrigerator trucks is proposed. • This system enables regenerative braking. • An innovative two-level controller is proposed for the power management system. • A fast dynamic programming technique to find real-time SOC trajectory is proposed. • In addition to idling elimination, this system reduces fuel consumption. - Abstract: Engine idling of refrigerator trucks during loading and unloading contributes to greenhouse gas emissions due to their increased fuel consumption. This paper proposes a new anti-idling system that uses two sources of power, battery and engine-driven generator, to run the compressor of the refrigeration system. Therefore, idling can be eliminated because the engine is turned OFF and the battery supplies auxiliary power when the vehicle is stopped for loading or unloading. This system also takes advantage of regenerative braking for increased fuel savings. The power management of this system needs to satisfy two requirements: it must minimize fuel consumption in the whole cycle and must ensure that the battery has enough energy for powering the refrigeration system when the engine is OFF. To meet these objectives, a two-level controller is proposed. In the higher level of this controller, a fast dynamic programming technique that utilizes extracted statistical features of drive and duty cycles of a refrigerator truck is used to find suboptimal values of the initial and final SOC of any two consecutive loading/unloading stops. The lower level of the controller employs an adaptive equivalent fuel consumption minimization (A-ECMS) to determine the split ratio of auxiliary power between the generator and battery for each segment with initial and final SOC obtained by the high-level controller. The simulation results confirm that this new system can eliminate idling of refrigerator trucks and reduce their fuel consumption noticeably such that the cost of replacing components is recouped in a short period of time.
[en] Highlights: • The limit of APU power changing rate significantly affects the fuel consumption. • The dynamic characteristic of APU is better when engine speed keeps constant. • Two types strategies are proposed to track the demand power of APU. - Abstract: Range-extended electric vehicles (REEVs) are becoming a development trend of new vehicles. Energy management is one of the core problems in REEVs. The structure and control method of the auxiliary power unit (APU) is determined based on the configuration analysis in this paper. An energy management optimization problem is proposed to solve the power distributions of APUs and batteries in the charge-sustaining (CS) stage of REEVs, which are determined by dynamic programming and pseudo-spectral optimal control, respectively. The results show that different limits of the APU power changing rate significantly influence fuel consumption. To obtain the power changing rate of APUs and to evaluate the energy management optimization method of REEVs, a model of the APU control system is built and verified by a platform test; the dynamic response characteristics and control parameters of the APU are obtained by step-changing conditions. Two types of strategies for tracking APU power are proposed for different power changing rates, and the fuel consumption of REEVs is analyzed in four types of driving cycles. The effect on fuel consumption caused by the power changing rate of the APU is verified.
[en] Highlights: • Novel instantaneous energy management strategy for series hybrid electric vehicles. • Finds a range for optimal equivalent factor of equivalent consumption minimization strategy. • The strategy needs less calibration in comparison with existing instantaneous strategies. • Simulation Model is developed based on experimental setup of powertrain components. - Abstract: This paper introduces a new energy management (EM) strategy for series hybrid electric vehicles (HEVs). Series HEVs operate in charge-depletion mode and then switch to the charge-sustaining mode in which the battery state of charge (SOC) is maintained within a certain range. The proposed EM strategy in this paper is a form of adaptive equivalent consumption minimization strategy (ECMS) that is designed for the charge-sustaining mode. The EM strategy defines soft bounds on the battery SOC and is penalized for exceeding these bounds. But, to catch energy-saving opportunities (CESOs), the EM strategy allows SOC to exceed the soft bounds. Thus, the introduced EM strategy is named ECMS-CESO. In addition, a range for the ECMS optimal equivalent factor is proposed for series HEVs. The proposed range is used in deriving the formula for calculating the adaptive equivalent factor. The main advantage of the proposed EM strategy is that ECMS-CESO can achieve close to optimal fuel economy without the need for predicting future driver demand. Since there is no need for prediction, the intensive calculations for finding the optimal control over the prediction horizon can be eliminated. Therefore, implementation of ECMS-CESO is easily feasible for real-time applications. Experimental powertrain data is collected to develop a powertrain model for a series HEV in this study. Simulation results on several drivecycles show that, on average, the fuel economy achieved by ECMS-CESO is within 6% of the maximum fuel economy. In addition, comparing ECMS-CESO with two existing adaptive ECMSs shows up to 5% improvement in fuel economy, on average.
[en] Two-layer control system of the spray booth is presented. Special attention is paid to the upper layer which optimizes operating point of the direct control layer to minimize the fuel consumption. The minimization is done on-line using measurements of the process variables and off-line identified models. In this way the actual distance to the limits of the process variables can be determined and the constraints can be shifted accordingly to determine a new set-point for the direct control layer. This algorithm assures safe performance of the system and minimizes the fuel consumption.
[en] Highlights: • A neural network model of fuel consumption in mining haul trucks was constructed and tested. • Using the cyclic activities, the model was able to predict unseen (testing) data. • Trucks idle times were identified as the most important unnecessary energy consuming portion of the network. • Practical remedies, based on the nature of mining operations, were proposed to reduce the energy consumption. - Abstract: Fuel consumption of mining dump trucks accounts for about 30% of total energy use in surface mines. Moreover, a fleet of large dump trucks is the main source of greenhouse gas (GHG) generation. Modeling and prediction of fuel consumption per cycle is a valuable tool in assessing both energy costs and the resulting GHG generation. However, only a few studies have been published on fuel prediction in mining operations. In this paper, fuel consumption per cycle of operation was predicted using artificial neural networks (ANN) technique. Explanatory variables were: pay load, loading time, idled while loaded, loaded travel time, empty travel time, and idled while empty. The output variable was the amount of fuel consumed in one cycle. Mean absolute percentage error (MAPE) of 10% demonstrated applicability of ANN in prediction of the fuel consumption. The results demonstrated the considerable effect of mining trucks idle times in fuel consumption. A large portion of the unnecessary energy consumption and GHG generation, in this study, was solely due to avoidable idle times. This necessitates implementation of proper actions/remedies in form of both preventive and corrective actions
[en] Highlights: •Vehicle hardware In-the-loop VHiL testing and validation is implemented in vehicle test bed. •Torque at the roller bench test is used to control the torque at wheels to reflect vehicle electrification symptoms. •Electrified powertrain with Equivalent Consumption Minimization Strategy is tested and validated using VHiL. •Fuel economy and power train performance is measured using high precision fuel measurement device. -- Abstract: Hybridization of automotive powertrains by using more than one type of energy converter is considered as an important step towards reducing fuel consumption and air pollutants. Specifically, the development of energy efficient, highly complex, alternative drive-train systems, in which the interactions of different energy converters play an important role, requires new design methods and processes. This paper discusses the inclusion of an alternative hybrid power train into an existing vehicle platform for maximum energy efficiency. The new proposed integrated Vehicle Hardware In-the-loop (VHiL) and Model Based Design (MBD) approach is utilized to evaluate the energy efficiency of electrified powertrain. In VHiL, a complete chassis system becomes an integrated part of the vehicle test bed. A complete conventional Internal Combustion Engine (ICE) powered vehicle is tested in roller bench test for the integration of energy efficient hybrid electric power train modules in closed-loop, real-time, feedback configuration. A model that is a replica of the test vehicle is executed – in real-time- where all hybrid power train modules are included. While the VHiL platform is controlling the signal exchange between the test bed automation software and the vehicle on-board controller, the road load exerted on the driving wheels is manipulated in closed –loop real-time manner in order to reflect all hybrid driving modes including: All Electric Range (AER), Electric Power Assist (EPA) and blended Modes (BM). Upon successful implementation of VHiL, a comparative study between Rule Based (RB) energy management strategy (EMS) and Equivalent Consumption Minimization Strategy (ECMS) to Control Parallel Through-The-Road Hybrid Electric Vehicle (PTTR-HEV) is performed. The study shows that the actual fuel efficiency of the tested vehicle under both control strategies can be used in order to evaluate the effectiveness of energy conversion efficiency of the powertrain system. The fuel consumption of hybridized powertrain is compared with the conventional powertrain equipped in an actual vehicle to help comprehend the degree of efficiency attained by the hybridization. This process is developed in order to enable effective tuning/validation of advanced energy management strategies utilized in hybrid electric powertrain through an evaluation of a complete real chassis system subject to electric hybridization. The VHiL is considered as new evolution for the utilization of vehicle test bed as a predictive mechatronic platform for the development of energy efficient electrified propulsion systems and thus reduce cost and time.
[en] Highlights: • Velocity is predicted by multi scale single step method with post-processing. • A state reconstitution method is proposed to tackle reference state deficiencies. • SMPC-based strategies with variable horizons are built to improve energy management for practical cycle. • HIL experiments with practical driving cycles are conducted to verify the strategy. - Abstract: Model predictive control (MPC) can effectively solve online optimization issues, even with various constraints, when maintained at high robustness. Considering the energy management issue of plug-in hybrid electric bus (PHEB) as a constrained nonlinear optimization problem, a strategy based on stochastic model predictive control (SMPC) is put forward and verified in this paper. Firstly, Markov Chain Monte Carlo Method (MCMC) is adopted to forecast velocity sequences at every current state, in the form of multi scale single step (MSSS), with post-processing algorithms to moderate fluctuations of the prediction results like average filtering, quadratic fitting, and the like. The offline simulation results show that the optimization can effectively improve the predictive accuracy, make the following energy management feasible and reduce the fuel consumption by 1.9%. Then the SMPC-based energy management strategy is proposed. In order to prevent the driving cycle state deficiencies from interrupting the prediction for practical application, a state reconstitution method is constructed accordingly. Besides, the predictive steps are made time-varying by an online accuracy estimation method and a corresponding threshold to maintain the accuracy of forecast. Finally, the hardware-in-the-loop (HIL) experiments are conducted and the results show that the SMPC-based strategy is reasonable and the fuel consumption decreases by 3.9% further with variable predictive steps than that of fixed ones. In summary, this paper illustrates an effective SMPC-based methodology for energy management for PHEB, and techniques like MSSS prediction with post-processing, state reconstitution method, online accuracy estimation can be adopted to solve similar problems.
[en] Highlights: • Operating characteristics of conventional and hybrid electric buses were examined. • Recovery of braking energy offers an excellent opportunity to improve fuel economy. • Speed and altitude profiles of routes have dramatic impacts on the energy recovery. • Capacity of the auxiliary power source has a dramatic impact on the energy recovery. • Round-trip efficiency of the regenerative braking system was calculated to be 27%. - Abstract: The basic operating characteristics of a conventional bus (CB) and a hybrid electric bus (HEB) were examined under urban driving conditions. To perform this examination, real-time operating data from the buses were collected on the Campus-Return route of the Sakarya Municipality. The main characteristics examined were the traction, braking, engine, engine generator unit (EGU), motor/generator (M/G), and ultracapacitor (Ucap) energies and efficiencies of the buses. The route elevation profile and the frequency of stop-and-go operations of the buses were found to have dramatic impacts on the braking and traction energies of the buses. The declining profile of the Campus-Return route provided an excellent opportunity for energy recovery by the regenerative braking system of the HEB. However, owing to the limits on the capacities and efficiencies of the hybrid drive train components and the Ucap, the bus braking energies were not recovered completely. Braking energies as high as 2.2 kW h per micro-trip were observed, but less than 1 kW h of braking energy per micro-trip was converted to electricity by the M/G; the rest of the braking energy was wasted in frictional braking. The maximum energy recovered and stored in the Ucap per micro-trip was 0.5 kW h, but the amount of energy recovered and stored per micro-trip was typically less than 0.2 kW h for the entire route. The cumulative braking energy recovered and stored in the Ucap for the Campus-Return route was 52% of the available brake energy, which was 13.02 kW h. Consequently, the round-trip efficiency of the regenerative braking system, between the wheels and Ucap, was determined to be 27%. Finally, although the brake engine energy (BEE) of the CB was 1.18 times higher than its positive traction energy (PTE), the BEE of the HEB was only 1.07 times higher than its PTE. In fact, it is normal to expect the BEE to be higher than the PTE owing to power train losses, but the energy recovered by the regenerative braking system was found to cover most of the power train losses and even improve the energy efficiency of the HEB