•Driving cycle construction methods are studied.•A bus-station-based driving cycle segment division method is proposed.•An improved whole-trip-based bus driving cycle construction method is ...formulated.•The typical hybrid electric bus driving cycles using improved and regular construction methods are compared.
Intra-city buses have become an effective way to alleviate urban traffic congestion. Developing the driving cycle for an intra-city hybrid electric bus is of great importance for energy economy and emissions performances tests, as well as control strategy optimization to enhance fuel economy and reduce emissions. In the construction of the bus driving cycle using the adapted regular method based on kinematic segments with fixed time-steps, the transition of partial kinematic segments leads to velocity fluctuation, which does not reflect the actual driving characteristics. This study initially collects driving data from a hybrid electric bus in Shanghai, and constructs its typical driving cycle by the k-means clustering method. Considering the characteristics of frequent stopping and repetition during the bus driving cycle, the bus-station-based driving cycle segment division method and the whole-trip-based bus driving cycle construction method are proposed. A typical hybrid electric bus driving cycle is then constructed using the proposed methods. Finally, the typical hybrid electric bus driving cycles developed by two methods are compared and analyzed. The 2-norm value of the characteristic parameter error vector is decreased from 0.249 for the typical hybrid electric bus driving cycle based on the adapted regular method to 0.163 for the improved method. It is shown that the whole-trip-based bus driving cycle construction method reflects the actual driving characteristics better, has lighter computation burden, and lays the foundation for the development of an adaptive control strategy for the hybrid electric bus as well as tests of its energy economy and emissions.
The rapid development of the transportation sector requires consideration of fossil fuel consumption and its negative effects. For this reason, electric vehicles have started to be an alternative to ...conventional ones that use fossil fuels. As a transportation fuel, hydrogen can be considered as a suitable alternative to fossil fuels. The durations for hydrogen refueling and fossil fuel refueling in conventional vehicles are close to each other. To promote the use of fuel cell vehicles, it is imperative to build the necessary facilities, including hydrogen fuel stations, that will support this need in the near future. In this study, the refueling of the hydrogen tank is simulated for the use of a fuel cell electric vehicle in real usage conditions and it takes 280 s to supply 4.34 kg of hydrogen to the tank of the vehicle, which is driven with a range of 650 km. The fuel economy of the vehicle is compared with conventional vehicles and was found to be 11.0 ¢/km and 10.2 ¢/km, respectively. Renewable supported production technologies are taken as a reference in hydrogen pricing, and these costs are even lower in hydrocarbon-based production. Depending on the source of the hydrogen, the emission intensity of the vehicle can be reduced to 4.7 gCO2/km with green hydrogen technologies, while it can be up to 166.8 gCO2/km in case of intensive fossil resource use. This study shows that a fuel cell vehicle can compete with traditional ones in terms of range, refueling time and fuel economy and is more environmentally friendly.
•An FCEV is simulated in the designed driving cycle based on real-world data.•Vehicle fuel consumption is 6.67 gH2/km or its gasoline equivalent is 2.51 L/100 km.•The vehicle can travel 650.3 km with 4.34 kg of hydrogen.•Refueling time for a 650 km trip takes 280 s.•The emission value of the vehicle is between 4.7 and 166.8 gCO2/km.
•The development of a World-wide harmonized Light duty Test Cycle (WLTC) is presented.•The WLTC was obtained from “real world” driving data.•WLTC contains speed phases instead of the traditional road ...categories.•WLTC was adapted to three vehicle categories with different PMR ratio.•WLTC represents average driving characteristics around the world.
This paper presents the World-wide harmonized Light duty Test Cycle (WLTC), developed under the Working Party on Pollution and Energy (GRPE) and sponsored by the European Union (with Switzerland) and Japan. India, Korea and USA have also actively contributed. The objective was to design the harmonized driving cycle from “real world” driving data in different regions around the world, combined with suitable weighting factors. To this aim, driving data and traffic statistics of light duty vehicles use were collected and analyzed as basic elements to develop the harmonized cycle. The regional driving data and weighting factors were then combined in order to develop a unified database representing the worldwide light duty vehicle driving behavior. From the unified database, short trips were selected and combined to develop a driving cycle as representative as possible of the unified database. Approximately 765,000km of data were collected, covering a wide range of vehicle categories, road types and driving conditions. The resulting WLTC is an ensemble of three driving cycles adapted to three vehicle categories with different power-to-mass ratio (PMR). It has been designed as a harmonized cycle for the certification of light duty vehicles around the world and, together with the new harmonized test procedures (WLTP), will serve to check the compliance of vehicle pollutant emissions with respect to the applicable emissions limits and to establish the reference vehicle fuel consumption and CO2 performance.
In order to reasonably allocate the power needs and manage the energy of the plug-in hybrid electric vehicle (PHEV) more efficiently, an energy management strategy (EMS) based on deep learning and ...improved model predictive control was proposed. Firstly, the vehicle energy flow test was carried out for the PHEV, and the multi-physics (mechanical-electrical-thermal-hydraulic) model was constructed and validated. Secondly, six prediction models were built based on different algorithms and the effects were compared and analyzed in detail. Finally, a long short-term memory based improved model predictive control algorithm (LSTM-IMPC) was developed, and the effects of three EMSs based on the charge-depleting charge-sustaining rule (CD-CS), dynamic programming (DP) and LSTM-IMPC were investigated under Worldwide Light-duty Test Cycle (WLTC), New European Driving Cycle (NEDC) and real driving cycle (RDC). The results show that the fuel-saving rates of the LSTM-IMPC-based EMS under these three cycles are respectively 3.81%, 5.6% and 18.71% compared with the CD-CS-based EMS, which prove the good fuel-saving performance and strong robustness of the proposed EMS. The fuel-saving rates of the LSTM-IMPC-based EMS are close to the DP-based EMS, which are the global optimal under these three cycles.
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•The multi-physics model for energy management of PHEV was built.•The effects of six prediction models were compared and analyzed in detail.•The LSTM-IMPC-based energy management strategy was proposed.•Fuel saving rate of LSTM-IMPC-based EMS under RDC reaches 18.71%.
Hybrid Electric Vehicles (HEVs) have been proven to be a promising solution to environmental pollution and fuel savings. The benefit of the solution is generally realized as the amount of fuel ...consumption saved, which by itself represents a challenge to develop the right energy management strategies (EMSs) for HEVs. Moreover, meeting the design requirements are essential for optimal power distribution at the price of conflicting objectives. To this end, a significant number of EMSs have been proposed in the literature, which require a categorization method to better classify the design and control contributions, with an emphasis on fuel economy, providing power demand, and real-time applicability. The presented review targets two main headlines: (a) offline EMSs wherein global optimization-based EMSs and rule-based EMSs are presented; and (b) online EMSs, under which instantaneous optimization-based EMSs, predictive EMSs, and learning-based EMSs are put forward. Numerous methods are introduced, given the main focus on the presented scheme, and the basic principle of each approach is elaborated and compared along with its advantages and disadvantages in all aspects. In this sequel, a comprehensive literature review is provided. Finally, research gaps requiring more attention are identified and future important trends are discussed from different perspectives. The main contributions of this work are twofold. Firstly, state-of-the-art methods are introduced under a unified framework for the first time, with an extensive overview of existing EMSs for HEVs. Secondly, this paper aims to guide researchers and scholars to better choose the right EMS method to fill in the gaps for the development of future-generation HEVs.
Field-relevant reference driving cycles, equivalent to real-life operation, are a prerequisite for the consistent development and testing of vehicles, their components, and control algorithms. ...Furthermore they are the basis for certification and type testing. However, a static cycle can easily be detected during vehicle testing, so that optimized control parameters could be used to obtain improved emission results under test conditions. In this paper, a novel method is described and applied to generate a dynamic driving cycle that statistically matches the real-life operation of a vehicle. The analysis is performed based on an extensive field data set obtained during an automated measurement campaign of public busses for more than a full year with 27,365h of operation and 315,583km driven in the city of Hamburg (Germany). The data collected is statistically compared to the static reference cycles New European Driving Cycle (NEDC) and Worldwide harmonized Light Vehicles Test Procedure (WLTP). Two micro trip models with increasing complexity are described and fit to the data set. All models are quantitatively compared to the measured data set applying a Quality of Fit (QoF) indicator. Based on the highest consistency to field data, a non-deterministic driving cycle generator is developed and its output is statistically compared to the original measurement. In contrast to the existing reference cycles, the dynamic output of the non-deterministic driving cycle generator presented in this paper is statistically proven to be consistent with real-life operation of public busses in the urban environment of Hamburg.
Hybrid electric vehicles (HEVs) feature multiple working modes. Thoughtful selection of these modes can optimally balance driving performance, power demands, and energy consumption, thereby enhancing ...the overall efficiency of the vehicle. This paper presents a soft actor-critic (SAC) approach trained on a multi-modal driving cycle (MDC) for selecting operational modes of electro-hydraulic hybrid electric vehicle (EHHEV). Firstly, characteristic parameters are extracted and clustered for five typical driving cycles through principal component analysis and K-means clustering, creating a multi-modal driving cycle. Secondly, based on the operational characteristics of EHHEV, state variables, action variables, reward functions, learning rates, and other parameters are set for the SAC algorithm, and the EMS framework is built based on the electro-hydraulic hybrid electric power system. Subsequently, the SAC algorithm is trained using the MDC to construct the SAC-MDC EMS. Results demonstrate that compared to EV, RB EMS, and SAC EMS, IREC achieves maximum improvements of 22.38 %, 5.55 % and 0.80 %, respectively. The dynamic performance and the motor load optimization capability are also enhanced. To further validate the practicality and reliability of the SAC-MDC EMS, this paper validates it using actual driving data, revealing that it still exhibits outstanding performance.
•A multi-modal driving cycle containing information about multiple driving situations is created.•The SAC algorithm is proposed for switching working modes in electro-hydraulic hybrid electric vehicles.•The output actions of the SAC algorithm are transformed into rule-based control to enhance algorithm interpretability.•The multi-modal driving cycle is applied to the training of the SAC algorithm to improve its adaptability.
•A specific driving cycle is constructed through a naturalistic data-driven method.•An energy management strategy based on the TD3 algorithm is proposed.•The health of the onboard lithium-ion battery ...system is taken into consideration.•Real velocity data and the constructed cycle are used as the training and testing datasets.•The superiority of the proposed strategy is validated compared with DDPG and DDQL.
Energy management is critical to reduce energy consumption and extend the service life of hybrid power systems. This article proposes an energy management strategy based on deep reinforcement learning with awareness of battery health for an urban power-split hybrid electric bus. In this article, a specific driving cycle of the test bus route is constructed through a naturalistic data-driven method to evaluate the practical operating costs of the hybrid electric bus accurately. Furthermore, an energy management strategy based on twin delayed deep deterministic policy gradient algorithm considering battery health is innovatively designed to minimize the total operating cost with a tradeoff between fuel consumption and battery degradation. Finally, the superiority of the proposed strategy over other state-of-the-art deep reinforcement learning-based strategies including deep deterministic policy gradient and double deep Q-learning is validated. Simulation results show that the constructed driving cycle can effectively reflect the real traffic conditions of the test bus route, and the proposed strategy can reduce the total operating cost while extending the battery life efficiently. This article makes contribution to the reliable evaluation of the practical operating costs and the extension of the battery life for urban hybrid electric buses through deep reinforcement learning methods.
Fuel cell (FC) technologies for mobility are gaining interest as promising options to decarbonize the transport sector in line with the current progress towards the H2 economy. Previous studies show ...how the fuel cell range extender (FCREx) powertrain architecture can offer flexible and efficient operation along with the potentially low total cost of ownership (TCO) in passenger car applications. Cradle-to-grave emissions of these vehicles have not been estimated, nor their variation with the components sizing or the H2 production pathway analyzed. In this study, the life cycle assessment (LCA) and sizing methodologies were combined to address these knowledge gaps. The design spaces were generated by varying the FC maximum power, the battery capacity and the H2 tank capacity and by simulating the resulting designs with the WLTC 3b driving cycle. Then, the lifetime H2 and energy consumption results and design parameters were calculated and used as inputs to estimate the greenhouse gases (GHG) and NOX emissions on the manufacturing and fuel production cycles. From the results, it was proven how considering steam methane reforming (SMR) with carbon capture and storage (CCS) as the H2 production pathway could decrease by 60% and 38% GHG-100 and NOX emissions respectively, with respect to electrolysis where electricity is generated with the EU mix. The optimum design, in terms of emissions, was found to be with low-moderate battery capacity and moderate-high FC maximum power in contrast to the optimum design for performance, which had high battery capacity and high FC stack power.
•A novel approach using sizing and LCA methodologies is used for passenger FCREx.•Design spaces for FCREx vehicles showing GHG-100, NOX and performance are generated.•Production pathways for H2 were electrolysis with the EU mix, SMR and SMR with CCS.•GHG-100 & NOX emissions may vary up to 10% when comparing the worst and best designs.•Emissions-wise and performance-wise optimum FCREx designs are compared
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