Series Hybrid Research Articles

Technology selection for holistic analysis of hybrid-electric commuter aircraft

Electric powertrains have different characteristics than conventional powertrains with combustion engines and require unconventional aircraft designs to evolve their full potential. Therefore, this paper describes a method to identify potential aircraft designs with electrified powertrains. Promising technology options in the fields of powertrain architecture, aerodynamic interactions, onboard systems and operating strategies were collected by the project partners of the LuFo project GNOSIS. The effect of the technology options on a commuter aircraft was evaluated in terms of global emissions (hbox {CO}_{2}), local emissions (text {NO}_{text {X}} and noise) and operating costs. The evaluation considers an entry into service in 2025 and 2050 and is based on the reference aircraft Beechcraft 1900D. Literature review and simplified calculations enabled the evaluation of the aerodynamic interactions, systems and operating strategies. A preliminary aircraft design tool assessed the different powertrain architectures by introducing the two parameters ’power hybridization’ and ’power split’. Afterwards, compatible technology options were compiled into technology baskets and ranked using the shortest euclidean distance to the ideal solution and the farthest euclidean distance to the worst solution (Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method). An analysis of the CS 23 regulations leads to a high-wing design and excluded the partial turbo-electric powertrain architecture with the gas turbine in the aircraft tail. For 2025, a partial turbo-electric powertrain with two additional electric driven wingtip propellers was selected. A serial hybrid powertrain, which uses a gas turbine or fuel cell in combination with a battery, powers distributed electric propulsors at the wing leading edge in 2050. In both scenarios, the aircraft design includes an electric environmental control system, an electric driven landing gear and electro-hydraulic actuators for the primary flight control and landing gear.

Construction of a Semantic Segmentation Network for the Overhead Catenary System Point Cloud Based on Multi-Scale Feature Fusion

Accurate semantic segmentation results of the overhead catenary system (OCS) are significant for OCS component extraction and geometric parameter detection. Actually, the scenes of OCS are complex, and the density of point cloud data obtained through Light Detection and Ranging (LiDAR) scanning is uneven due to the character difference of OCS components. However, due to the inconsistent component points, it is challenging to complete better semantic segmentation of the OCS point cloud with the existing deep learning methods. Therefore, this paper proposes a point cloud multi-scale feature fusion refinement structure neural network (PMFR-Net) for semantic segmentation of the OCS point cloud. The PMFR-Net includes a prediction module and a refinement module. The innovations of the prediction module include the double efficient channel attention module (DECA) and the serial hybrid domain attention (SHDA) structure. The point cloud refinement module (PCRM) is used as the refinement module of the network. DECA focuses on detail features; SHDA strengthens the connection of contextual semantic information; PCRM further refines the segmentation results of the prediction module. In addition, this paper created and released a new dataset of the OCS point cloud. Based on this dataset, the overall accuracy (OA), F1-score, and mean intersection over union (MIoU) of PMFR-Net reached 95.77%, 93.24%, and 87.62%, respectively. Compared with four state-of-the-art (SOTA) point cloud deep learning methods, the comparative experimental results showed that PMFR-Net achieved the highest accuracy and the shortest training time. At the same time, PMFR-Net segmentation performance on S3DIS public dataset is better than the other four SOTA segmentation methods. In addition, the effectiveness of DECA, SHDA structure, and PCRM was verified in the ablation experiment. The experimental results show that this network could be applied to practical applications.

Comparative analysis of dual second-order generalized integrator-phase locked loop based series hybrid filter and static synchronous compensator for load voltage compensation and power quality improvement with grid synchronization in islanded microgrid

PurposeThe purpose of this study is to eliminate voltage harmonics and instantly measure the positive sequence fundamental voltage during unbalanced grid conditions, the dual second-order generalized integrator-phase locked loop used in series hybrid filter structures is often used in grid synchronisation in three-phase networks. The preferred series active hybrid power filter simultaneously compensates for voltage balancing and current harmonics generated by non-linear loads.Design/methodology/approachThis paper examines the use of renewable energy–based microgrid (MG) to support linear and non-linear loads. It is capable of synchronising with both the utility and the diesel generator unit. Power is transferred from the grid throughout a stable grid situation with minimum renewable energy generation and maximum load demand. It synchronises with diesel generator set to supply the load and form an AC MG during outages and minimum renewable power generation. In islanded and grid-connected mode, the voltage and power quality issues of the MG are controlled by static synchronous compensator and series hybrid filter.FindingsBecause of the presence of non-linear loads, reactive loads in the distribution system and the injection of wind power into the grid integrated system result power quality issues like current harmonics, voltage fluctuations, reactive power demand, etc.Originality/valueThe voltage at the load (linear and non-linear) is regulated, and the power factor and total harmonic distortions were improved with the help of the series hybrid filter.

Analysis on the potential of EGR strategy to reduce fuel consumption in hybrid powertrains based on advanced gasoline engines under simulated driving cycle conditions

The increased concern for environmental problems has boosted the electrification of passenger cars to remove air pollutant emissions from urban areas. Automotive manufacturers have predominantly opted for hybrid powertrains with advanced gasoline engines, because of the current limitations of battery electric vehicles and the higher costs of diesel aftertreatment systems. Meanwhile, the exhaust gas recirculation (EGR) strategy decreases fuel consumption and CO2 emissions in gasoline engines. The powertrain hybridization may increase the EGR benefit in fuel consumption, given that the dependence of internal combustion engine (ICE) operation on the driver’s power demand is reduced. The ICE can usually operate at medium loads, around its maximum efficiency zone, where the EGR benefit is greater than at low loads. Therefore, this research aimed to quantify the fuel saving achieved with EGR in a gasoline-electric hybrid powertrain under driving cycle conditions. To this end, vehicle 0D simulations were performed using a map-based engine fuel consumption model. Engine tests and 1D simulations were carried out to obtain ICE fuel maps with and without EGR. Besides, the transient performance of the vehicle 0D model was validated with experimental data. Both rule- and optimization-based strategies were used to manage the power split between the engine and electric motor. Modeling results revealed that EGR improves fuel economy by 4.6% in the hybrid powertrain during a WLTP class 3b driving cycle, 2% more than in the conventional one.

Powertrain Hybridization and Parameter Optimization Design of a Conventional Fuel Vehicle Based on the Multi-objective Particle Swarm Optimization Algorithm

Powertrain Hybridization and Parameter Optimization Design of a Conventional Fuel Vehicle Based on the Multi-objective Particle Swarm Optimization Algorithm

Visual Detection and Deep Reinforcement Learning-Based Car Following and Energy Management for Hybrid Electric Vehicles

Practical vision-based technology is essential for the autonomous driving of intelligent hybrid electric vehicles. In this article, a hierarchical control structure is proposed, which combines you only look once-based object detection and learning-based intelligent control by deep reinforcement learning. After modeling a typical car-following scene, the leading car is detected in the driving image, and the real-time distance between two cars is evaluated by vision-based distance measurement. Then, a deep Q-network is adopted to learn the car-following control strategy and energy management strategy, which achieves multiobjective control of the hybrid powertrain while maintaining a reasonable distance for the following car. When completing off-line training, the online processor-in-the-loop test by the edge computing device NVIDIA Jetson AGX Xavier is performed. Results show that the hierarchical control strategy gets a fuel economy of 5.76 L/100 km while keeping a safe following distance. Moreover, the time consumed to run a driving cycle of 1797 s in the embedded device is 476.87 s, which means that a control loop, including target recognition, distance measurement, car-following control, and energy management, takes 0.26 s. This study proves that vehicle vision can lay the technical foundation for intelligent driving, and the results illustrate that the hierarchical control structure is capable of achieving considerable computing efficiency on embedded devices and has the potential for real vehicle control.

Mastering Complexity - Pre-competitive Collective Research in Hybrid Powertrains

Mastering Complexity - Pre-competitive Collective Research in Hybrid Powertrains

Modeling and total cost optimization of battery thermal management system in a hybrid electric vehicle

In the present study, the battery thermal management of a series hybrid electric vehicle is modeled and optimized for different standard driving conditions. The heat generation of the battery is comprehensively studied in previous works for the steady-state charge or discharge of the battery. Despite the previous investigations, the heat generation of the battery in the present study is modeled for the real driving conditions in terms of battery state of charge and power consumption. The model is validated with the experimental data. The validated model is implemented to minimize the total cost of ownership of the battery thermal management system. In this way, the battery thermal management system is optimized for three different standard driving cycles. The independent variables are the compressor speed, fan speed, chiller size, radiator size, and condenser size. The results reveal that the total cost of optimization is converged to 200 US dollars for all driving cycles. The optimum configuration of the battery thermal management system does not considerably vary for different driving cycles. Therefore, one of the optimum configurations can be considered the overall optimum for battery thermal management.

Wear behaviour of ceramic particle reinforced atmospheric plasma spray coatings on the cylinder running surface of internal combustion engines

Atmospheric plasma spray coatings can provide a solution for corrosion and wear resistant cylinder coating surfaces in hybrid powertrains. This article presents experimental results from a model study of metal matrix composite coating samples of chromium steel with varied ceramic content, in order to characterize the effect of hard particles and porous coating structure on friction and wear. Experiments were conducted on a high-frequency reciprocating rig with coated cast-iron cylinder segments and hard chromium coated piston ring segments. Samples were investigated under continuous and scarce lubrication conditions. A ceramic content of 35 wt% was found to be ideal in terms of friction and wear. Coatings with a higher ceramic content exhibited severe abrasive wear, whereas a ceramic content under 35 wt% allowed for increased adhesion between the ring and cylinder surfaces. A detailed investigation of focused ion beam milled sections of the coated cylinder wall segments revealed a stabilizing effect of the ceramic particles, which reduces the delamination of the coating structure.

Lean Burn Flame Kernel Characterization for Different Spark Plug Designs and Orientations in an Optical GDI Engine

Lean burn spark ignition (SI) engines represent an effective solution for improving fuel economy and reducing exhaust emissions and can be implemented both in conventional and hybrid powertrains. On the other hand, lean operation increases cyclic variability with negative impact on power output, engine efficiency, roughness, and operating stability. Although this phenomenon has been widely investigated, the effects of flow field on the inception and development of flames in direct injection spark ignition (DISI) engines under lean burn conditions is not yet completely understood. In particular, the effect of spark plug geometry and electrode orientation with respect to tumble motion has been minimally investigated. For these reasons, two different spark-plug geometries (i.e., single- and double-ground electrode) and three different orientations (i.e., cross-, counter-, and uni-flow with respect to the direction of tumble motion) were investigated in an optically accessible DISI engine for understanding their influence on the initial phase of combustion. The relative air–fuel ratio (AFRrel) was changed from stoichiometric to lean burn (1.00 to 1.30) for different spark timings around the maximum brake torque setting at fixed engine speed (2000 rpm). An image processing procedure was developed for evaluating the morphological parameters of flame kernels and studying the effects of spark plug design on engine operating stability. With a focus on the correlation between the position where ignition occurs with the subsequent locations of the flame kernel during the first phases of the combustion process, the analysis allowed the gathering of a better understanding of the influence that the electrodes’ geometries and orientation can have on the first stages of combustion development.

Study of a Gas Turbine Cycle to Boost the Autonomy of Electric Cars

The greenhouse gas emissions from the transportation sector are the primary cause of climate change. As a result, many studies have developed new powertrains with reduced CO2 emissions for the automotive industry. The gas turbine cycle coupled to an alternator is an autonomy booster for series hybrid electric vehicles. Many gas turbine configurations are proposed in the literature to obtain the highest cycle efficiency. This paper suggests a new architecture offering higher efficiency than all the previous cycles. The two-step methodology consists at first of a sensibility analysis using VBA and Refprop to determine the optimal operating conditions in terms of higher efficiency. The selected cycle consists of two compression stages with an intercooler, a combustion chamber, a cooled hot pressure turbine, an uncooled low-pressure turbine, and a recuperator. The efficiency of this design reaches 51.39%, which approximately matches the designs compared in the literature, but is more compact because it does not require a second combustion chamber.

An Energy Management Strategy of Power-Split Hybrid Electric Vehicles Using Reinforcement Learning

With the rapid development of science and technology, the automobile industry is also gradually expanding due to which energy security and ecological security are seriously threatened. This paper was aimed at studying the energy organization strategy of power-split hybrid electric vehicles based on a reinforcement learning algorithm. A power-split hybrid electric vehicle (HEV) combines the advantages of both series and parallel hybrid vehicle architectures by using a planetary gear set to split and combine the power generated by electric machines and a combustion engine. This improves the fuel economy to some extent. However, to increase the fuel economy to a greater extent. This study primarily introduces the hybrid electric vehicle’s structure and presents a reinforcement learning-based management of energy approach for hybrid electric vehicles. It constructs the vehicle power model of HEV and Markov probability transfer model, then designs the energy control strategy based on reinforcement learning, and finally compares it with the energy control strategy based on PID (Proportional-Integral-Derivative). Using MATLAB/Simulink, the cycle conditions of NEDC (New European Driving Cycle) and FTP-75 (Federal Test Procedure) are selected to carry out simulation experiments. The energy management technique suggested in this study, which is based on reinforcement learning, may efficiently enhance the usage rate of automotive gasoline. The replication results show that the fuel consumption per 100 km (kilometers) based on reinforcement learning management strategy is 4.6% and 2.7% lower than the PID management strategy under two working conditions. The economy of fuel of the hybrid electric vehicle is also effectively improved.

Feasibility Study of Electrified Light-Sport Aircraft Powertrains

A theory-based aerodynamic model developed and applied to electrified powertrain configurations was intended to analyze the feasibility of implementing fully electric and serial hybrid electric propulsion in light-sport aircraft. The range was selected as the primary indicator of feasibility. A MATLAB/Simulink environment was utilized to create the models, involving the combination of proportional-integral-derivative controllers, aerodynamic properties of a reference aircraft, and powertrain limitations taken from off-the-shelf components. Simulations conducted by varying missions, batteries, fuel mass, and energy distribution methods provided results showcasing the feasibility of electrified propulsion with current technology. Results showed that the fully electric aircraft range was only 5% of a traditionally powered aircraft with current battery technology. Hybrid electric aircraft could achieve 44% of the range of a traditionally powered aircraft, but this result was found to be almost wholly related to fuel mass. Hybrid electric powertrains utilizing an energy distribution with their optimal degree of hybridization can achieve ranges up to 3% more than the same powertrain utilizing a different energy distribution. Results suggest that improvements in the power-to-weight ratio of the existing battery technology are required before electrified propulsion becomes a contender in the light-sport aircraft segment.

Impact of low carbon fuels (LCF) on the fuel efficiency and NOx emissions of a light-duty series hybrid commercial delivery vehicle

This work evaluates the potential of using four different low carbon fuels (LCF) in a series hybrid vehicle concept and compares the results to a conventional diesel combustion counterpart. To do this experimental data from a low NOx emission calibration is obtained for each of the different fuels, and 0-D vehicle simulations of an OPEL Movano van model are made to evaluate the performance, in terms of fuel consumption and engine-out NOx, during the Worldwide harmonized Light vehicles Test Cycles (WLTC) using a simplified engine map strategy. The vehicle selection allows to evaluate the scenario of a delivery application with three different payloads 0%, 50% and 100%. The work is motivated by the current automotive industry’s need to reduce emissions and use energy resources efficiently, evaluating different strategies to fulfil both objectives. The evaluation of different energy sources –such as LCF– and powertrains, have been extensively researched topics, however the information is more scarce using a combination of both strategies. The results from this work show that series hybrid vehicle presents a reduction of fuel consumption of up to 5% with 100% payload, across all fuels tested. Nonetheless, engine-out emission levels of NOx show 16% worse performance for the hybrid case due to its operation at higher engine speeds and loads during the charging of the battery.

Life-Cycle CO2-Equivalent Emissions of Cars Driven by Conventional and Electric Propulsion Systems

As an important trend in the automotive industry, electrification of propulsion systems has potential to significantly reduce greenhouse-gas emissions of the transportation sector. Whereas electric vehicles do not produce exhaust emissions during driving, the impact of electricity provision for charging batteries, as well as the impact of vehicle production play an essential role in a holistic consideration of the carbon footprint. The paper introduces a comprehensive evaluation of greenhouse gas-emission-related factors of cars driven by different propulsion technologies, considering the entire product life cycle. This comprises vehicle production, including battery system, electric powertrain and other relevant components, the car’s use phase under consideration of different electricity mixes and the end-of-life phase. The results of the study give insights of influencing factors on life-cycle-related carbon-dioxide-equivalent emissions of cars driven by combustion engines, hybrid powertrains and battery-electric propulsion systems. In addition, a comparison of actual mass-production cars is made and the total life-cycle carbon footprints are discussed under different boundary conditions of electric power supply. In this way, the article comprehensively introduces an automotive life-cycle assessment and provides fundamental information, contributing to an objective discussion of different propulsion technologies.

Development of a Control System for Permanent Magnet Synchronous Motor Based on LabVIEW and FPGA

<div class="section abstract"><div class="htmlview paragraph">With the strict requirements of harmful emission regulations, carbon peaking and neutralization goal, the internal combustion engine (ICE) industry is facing great challenges. Compared with pure ICE powertrain, hybrid powertrain has the advantages on fuel consumption and harmful emissions, which is more suitable for the market today.</div><div class="htmlview paragraph">In series hybrid powertrain, because of the direct mechanical connection between ICE and motor, the motor can be used as an assistant in optimizing the performance of ICE. In order to realize the cycle-based or crank angle-based control of ICE, a high-frequency motor control system need to be built. Field Programmable Gate Array (FPGA) has the characteristics of high calculation frequency and high reliability to meet the demand. At the same time, the ICE control based on LabVIEW and FPGA has been realized. In order to realize the high-frequency co-control of ICE and motor, this paper developed a high-frequency and high-precision control system for permanent magnet synchronous motor (PMSM) based on LabVIEW and FPGA with vector control technology. Its functions include closed control loops for stator current and PMSM rotating speed. Then, based on NI Compact RIO platform, the hardware system of motor control is built and some verification experiments are done. In the constant voltage-frequency ratio speed open-loop test, current closed-loop test and speed closed-loop test, it is found that the speed response of the motor is very rapid, the overshoot and the steady state fluctuation are low.</div></div>

Development of a Series Hybrid Electrified Powertrain for a High Speed Tracked Vehicle Based on Driving Cycle Simulation

Development of a Series Hybrid Electrified Powertrain for a High Speed Tracked Vehicle Based on Driving Cycle Simulation

Research on start-up characteristics of electric motor driven reversely by hydraulic pump/motor

The series electro-hydraulic hybrid powertrain can improve the power characteristics of battery rail vehicles (BRVs) while also extending battery life and increasing vehicle mileage. A novel electric motor start-up method using the hydraulic pump/motor to reversely drive the permanent magnet synchronous motor (PMSM) to restart at a speed is proposed based on the energy reverse transfer characteristics between the electric motor and hydraulic pump/motor. PID controls the reverse driving process of the hydraulic motor. The zero vector voltage (ZVV) method is used to determine the initial speed and rotor position of the motor restart at a speed. The electric motor speed control adopts the speed sensorless space vector pulse width modulation (SVPWM) control based on the back-electromotive force (EMF) integration method. The simulation models are carried out in MATLAB/Simulink and AMESim and built a special test platform. The results show that the method for controlling electric motor restart is feasible and effective. The start-up current of the electric motor is related to its initial speed. The closer the initial speed is to its target speed, the smaller its start-up current is, the faster the electric motor reaches the stable target speed.

Model-Based Analysis of Different Equivalent Consumption Minimization Strategies for a Plug-In Hybrid Electric Vehicle

Plug-in hybrid electric vehicles (PHEVs) are developed to reduce fuel consumption and the emission of carbon dioxide. Common powertrain configurations of PHEVs (i.e., the configuration of the combustion engine, electric motor, and transmission) can be operated either in series, parallel, or power split hybrid mode, whereas powertrain configurations with multimode transmissions enable switching between those modes during vehicle operation. Hence, depending on the current operation state of the vehicle, the most appropriate mode in terms efficiency can be selected. This, however, requires an operating strategy, which controls the mode selection as well as the torque distribution between the combustion engine and electric motor with the aim of optimal battery depletion and minimal fuel consumption. A well-known approach is the equivalent consumption minimization strategy (ECMS). It can be applied by using optimizations based on a prediction of the future driving behavior. Since the outcome of the ECMS depends on the quality of this prediction, it is crucial to know how accurate the predictions must be in order to obtain acceptable results. In this contribution, various prediction methods and real-time capable ECMS implementations are analyzed and compared in terms of the achievable fuel economy. The basis for the analysis is a holistic model of a state-of-the-art PHEV powertrain configuration, comprising the multimode transmission, corresponding powertrain components, and representative real-world driving data.

1D and Map-Based Modeling Approaches for Railway Compression Ignition Engine in NRTC Application

Internal combustion engine-driven railway vehicles play an important role in the sector even today, due to the incomplete electrification of railway routes. However, stringent COP26 environmental rules are driving manufacturers and the scientific community to study more complex or alternative propulsion systems. Therefore, the design of new powertrains is becoming more challenging. Affordable, along with robust, development tools are fundamental for their development and optimization. In this framework, numerical simulation can represent an effective instrument to face these requirements. The proposed study assesses the accuracy of different modelling approaches for the same engine. In particular, a detailed 1D model, a simplified 1D model and a map-based model are compared. Although studies on engine simulation are available in the technical literature, the novelty introduced with this work is the assessment of accuracy and computational times of the engine models, considered by performing the new emission standard Non-Road Transient Cycle (NRTC), which is applied to a specific field such as Heavy Duty (HD) Compression Ignition (CI) engines for railway applications. This study provides new and quantitative results rarely available in the specific literature. The results show that the simplest model, despite its lower accuracy, maintains good predictive results in terms of cumulative fuel consumption and cumulative nitric oxide (NOx) emissions over the cycle considered. In particular, the difference in terms of fuel consumption for the map-based model is within 5% compared with the more detailed models. Moreover, the computational effort required by the simplest model is three orders of magnitude lower compared with the more detailed model. Therefore, as the simulation run-time is the priority, the simplest modeling approach is suitable for the evaluation of the global performance, in view of a more complex systems simulation, such as a hybrid powertrain.