Series Hybrid Research Articles

A free piston engine generator powered hybrid wheel loader with independent electric drive

Series hybrid powertrain is a practical way for wheel loader electrification. Conventional internal combustion engine (ICE) technique is mature but the efficiency is limited. Free piston engine generator (FPEG) is regarded as a promising technology that can be applied as decarbonized range extenders attributed to its high thermal efficiency and ultimate fuel flexibility. In this study, a FPEG-based series hybrid powertrain is proposed for wheel loaders to reduce fuel consumption. Multiple FPEG units are parallel connected to power the electric powertrain. The optimal unit commitment of the FPEG set is investigated via dynamic programming. A rule-based strategy called discrete power follower (DPF) is proposed. The influence of FPEG unit number on powertrain performances is studied. Energy efficiency comparison between the FPEG-based and the ICE-based series hybrid powertrains is carried out. Results show that the fuel consumption of the proposed FPEG-based series hybrid powertrain is 10.5 % lower than the ICE-based powertrain. The mean efficiency of wheel loader powertrain improves from 31.7 % to 35.1 %.

Examining Real-Road Fuel Consumption Performance of Hydrogen-Fueled Series Hybrid Vehicles

The use of hydrogen fuel produced from renewable energy sources is an effective way to reduce well-to-wheel CO2 emissions from automobiles. In this study, the performance of a hydrogen-powered series hybrid vehicle was compared with that of other powertrains, such as gasoline-powered hybrid, fuel cell, and electric vehicles, in a simulation that could estimate CO2 emissions under real-world driving conditions. The average fuel consumption of the hydrogen-powered series hybrid vehicle exceeded that of the gasoline-powered series hybrid vehicle under all conditions and was better than that of the fuel cell vehicle under urban and winding conditions with frequent acceleration and deceleration. The driving range was longer than that of the battery-powered vehicle but approximately 60% of that of the gasoline-powered series hybrid. Regarding the life-cycle assessment of CO2 emissions, fuel cell and electric vehicles emitted more CO2 during the manufacturing process. Regarding fuel production, CO2 emissions from hydrogen and electric vehicles depend on the energy source. However, in the future, this problem can be solved by using carbon-free energy sources for fuel production. Therefore, hydrogen-powered series hybrid vehicles show a high potential to be environmentally friendly alternative fuel vehicles.

Intelligent Setpoint Optimization for a Range Extender Hybrid Electric Vehicle with Opposed Piston Engine

Abstract This paper presents the exploration and optimization of a hybridized opposed piston (OP) engine. In this work, the exhaust crankshaft lead (ECL) is introduced as a controllable parameter in the hybridized OP engine enabled by eliminating the conventional geartrain linking the two crankshafts of an OP engine. This allows for variation in the effective compression and expansion ratio of the engine, along with scavenging performance. This novel control actuator as well as the adjustable speed and load setpoint in a series hybrid OP engine powertrain architecture necessitates an intensive calibration effort to realize any possible efficiency improvements. However, the OP engine within this series hybrid powertrain does not need to operate in highly transient conditions, but rather its operating point is fixed or slowly varying. This property permits using online calibration techniques. After manually sweeping speed and ECL values at two power setpoints, the use of an extremum seeking type inter-cycle optimization algorithm to optimize the operating setpoint is validated, showing that near optimal speed and ECL setpoints can be selected despite the relatively flat operating map of the OP engine.

Synergies and Trade-Offs in Hybrid Propulsion Systems Through Physics-Based Electrical Component Modeling

Abstract Hybrid-electric propulsion is recognized as an enabling technology for reducing aviation's environmental impact. In this work, a serial/parallel hybrid configuration of a 19-passenger commuter aircraft is investigated. Two underwing-mounted turboprop engines are connected to electrical branches via generators. One rear fuselage-mounted electrically driven ducted fan is coupled with an electric motor and respective electrical branch. A battery system completes the selected architecture. Consistency in modeling accuracy of propulsion systems is aimed for by development of an integrated framework. A multipoint synthesis scheme for the gas turbine and electric fan is combined with physics-based analytical modeling for electrical components. Influence of turbomachinery and electrical power system design points on the integrated power system is examined. An opposing trend between electrical and conventional powertrain mass is driven by electric fan design power. Power system efficiency improvements in the order of 2% favor high-power electric fan designs. A trade-off in electrical power system mass and performance arises from oversizing of electrical components for load manipulation. Branch efficiency improvements of up to 3% imply potential to achieve battery mass reduction due to fewer transmission losses. A threshold system voltage of 1 kV, yielding 32% mass reduction of electrical branches and performance improvements of 1–2%, is identified. This work sets the foundation for interpreting mission-level electrification outcomes that are driven by interactions on the integrated power system. Areas of conflicting interests and synergistic opportunities are highlighted for optimal conceptual design of hybrid powertrains.

A two-layer energy management system for a hybrid electrical passenger ship with multi-PEM fuel cell stack

The hybrid combination of hydrogen fuel cells (FCs) and batteries has emerged as a promising solution for efficient and eco-friendly power supply in maritime applications. Yet, ensuring high-quality and cost-effective energy supply presents challenges. Addressing these goals requires effective coordination among multiple FC stacks, batteries, and cold-ironing. Although there has been previous work focusing it, the unique maritime load characteristics, variable cruise plans, and diverse fuel cell system architectures introduce additional complexities and therefore worth to be further studied. Motivated by it, a two-layer energy management system (EMS) is presented in this paper to enhance shipping fuel efficiency. The first layer of the EMS, executed offline, optimizes day-ahead power generation plans based on the vessel's next-day cruises. To further enhance the EMS's effectiveness in dynamic real-time situations, the second layer, conducted online, dynamically adjusts power splitting decisions based on the output from the first layer and instantaneous load information. This dual-layer approach optimally exploits the maritime environment and the fuel cell features. The presented method provides valuable utility in the development of control strategies for hybrid powertrains, thereby enabling the optimization of power generation plans and dynamic adjustment of power splitting decisions in response to load variations. Through comprehensive case studies, the effectiveness of the proposed EMS is evaluated, thereby showcasing its ability to improve system performance, enhance fuel efficiency (potential fuel savings of up to 28%), and support sustainable maritime operations.

Energy-optimization design and management strategy for hybrid electric non-road mobile machinery: A case study of snowblower

Reduced reliance on fossil fuels is a critical issue, and electrification of agricultural machinery is a solution for lowering greenhouse gas emissions in non-road transportation. By separating the load and drive from the engine, electrification the implement allows the engine to operate at higher efficiency. This study suggests a series hybrid design that combines a snow blower with battery support and a tractor with a reduced engine size to meet the fuel consumption reduction target while maintaining its performance. Accordingly, the Particle Swarm Optimization (PSO) algorithm is employed to size the battery that meets the constraints. Additionally, the motors of actuators are designed based on efficiency maps. Moreover, the rule-based energy management strategy is also proposed to assess the solution. This suggestion is analyzed using simulation and compared to a traditional tractor as a benchmark. The simulation results demonstrate the effectiveness of the strategy in lowering emissions from non-road machinery. Furthermore, this approach can be applied to various different types of implements attached to the tractor.

LSTM-based energy management algorithm for a vehicle power-split hybrid powertrain

Recurrent neural networks (RNNs) have been used for vehicle speed prediction, trajectory prediction and state diagnosis. As a variant of RNNs, long short-term memory (LSTM) has better state memory ability to deal with problems that have sequential characteristics. In this study, a neural network with two substructures was developed and LSTM was used as the core part for a power-split hybrid powertrain energy management problem, and the optimal control data obtained from the dynamic programming (DP) algorithm was used as the training set. Then on a vehicle model established on coasting-down test data, LSTM conducted real-time control. This paper analyzes the battery status, fuel consumption, carbon dioxide emissions and operating condition of the engine and motor. The results showed that LSTM has good generalization ability and real-time control capability. In the test driving cycles of CHTC-LT and C-WTVC, the theoretical optimal scheme given by the DP algorithm is only 12.15% and 17.96% lower than the LSTM scheme in terms of fuel consumption. In addition, the influence of network size, training epochs and training set on energy-saving effect is compared in detail, it was found that the relatively optimal model required 128 LSTM layer units and 500 training epochs.

Design and Control of a Photovoltaic Distribution System Based on Modular Buck-Boost Converters

The main contribution of this research is the design of a series hybrid topology for a photovoltaic distribution system using Buck-Boost converter modules. This design incorporates a maximum power point tracking (MPPT) algorithm based on the perturb and observe method, linear PI controllers, and an energy management algorithm. The controllers' design is validated through simulation using PSIM and SISOTOOL/MATLAB. This work aims to achieve active power-sharing in the AC grid through a control loop implemented with a three-phase inverter. The validation of the topology and controller design demonstrates tracking and robustness in four test scenarios for the state variables in microgrids: constant and variable irradiance conditions, auxiliary storage device (ASD) protection and control loops, and power-sharing with the AC grid, while considering the DC system dynamics.

Research on Emotion Recognition Model of Takeaway Evaluation Text Based on LSTM-CNN

Takeaway evaluation is a literal evaluation of the products and services experienced by consumers, which not only provides a realistic basis for the improvement of products and services of merchants, but also affects the purchase decision of consumers in the future. In this paper, NLP(natural language processing) is used for preprocessing in tensorflow environment, and an emotion recognition model of takeaway evaluation text based on LSTM-CNN is established. The model is based on Bi-LSTM and attention mechanism, and uses Word2Vec method to vectorize text vocabulary. Then, the LSTM-CNN serial hybrid model is used to extract the context and local semantics of the text, and the effectiveness of the algorithm is verified by an example. The precision reaches 0.937, the recall rate reaches 0.896,F1 and the F1 value reaches 0.906. Through horizontal comparison, it can be found that the model in this paper is outstanding in this task, and the emotion enhancement model also improves the classification accuracy to some extent.

A Review of Powertrain Electrification for Greener Aircraft

This review proposes an overview of hybrid electric and full electric powertrains dedicated to greener aircraft in the “sky decarbonization” context. After having situated the state of the art and context of energy hybridization in the aviation sector, we propose the visit of several architectures for powertrain electrification, situating the potential benefits but also the main challenges to be faced to takeoff these new solutions. Then, as a first example, we consider the EU project “HASTECS” (Hybrid Aircraft: reSearch on Thermal and Electric Components and Systems) in the framework of Clean Sky 2. It relates to a series hybrid chain integrated into a regional aircraft. This energy system integrates especially power electronics and electric machines with a high degree of integration, which raises the “thermal challenge” and the need to integrate cooling devices. Through the snowball effects typical of the aviation sector, this example emphasizes how important it is to “hunt for kilos”, an alternative solution consisting of eliminating the power electronics within the powertrain. This is why we propose a second example, which concerns an AC power channel without power electronics that only integrates synchronous magnet machines (generator and motor) directly coupled on an AC bus. This last architecture nevertheless raises questions in terms of stability, with one solution being to insert an auxiliary hybridization branch via battery storage. Theoretical analyses and experiments at a reduced power scale show the viability of this concept. Finally, some recommendations for future research with potential technological breakthroughs complete that review.

Investigation of drive cycle simulation performance for electric, hybrid, and fuel cell powertrains of a small-sized vehicle

The powertrain simulations of electric, hybrid, and fuel cell hybrid vehicles for the determination of vehicle performance and powertrain efficiency have been studied in the last few decades. Related investigations on fuel cell hybrid vehicles mainly focus on the improvement of a specific powertrain component or vehicle performance for a specific hybrid type. Therefore, this study aims to investigate the effects of not only a specific hybrid configuration but also various electric and fuel cell hybrid powertrain alternatives comparatively including fuel cell, battery, and supercapacitor as energy sources on component efficiency via drive cycle simulations depending on the same vehicle parameters and capacity of the components. Four powertrain cases that differ from each other in terms of the electric power sources (battery, supercapacitor, and fuel cell) were modeled and simulated according to the ECE-15, JPN 10–15, and WLTP drive cycle using MATLAB® - Simulink® environment. The simulations gave the power outputs of powertrain components and component efficiencies, state of charge of the battery, hydrogen consumption, and airflow rate of the fuel cell during the drive cycle. The results showed that better battery efficiency is obtained in Case-1; better vehicle range and lower battery power output than Case-1 can be achieved in Case-2; better fuel cell efficiency is obtained in Case-3; and the lowest H2 consumption is obtained in Case-4. Thus, desired powertrain performance and powertrain efficiency can be achieved by choosing the proper powertrain component in hybrid vehicles.

Internet-Distributed Hardware-in-the-Loop Simulation Platform for Plug-In Fuel Cell Hybrid Vehicles

In order to simulate a PHEV’s dynamic characteristics with high fidelity and study the degradation process of a PHEV’s power sources in real-world driving conditions, an Internet-distributed hardware-in-the-loop (ID-HIL) simulation platform for PHEVs is established. It connects several geographically distributed hardware-in-the-loop (HIL) subsystems (including an in-loop vehicle, Cloud server, driving motor, fuel cells, and lithium battery) via the Internet to simulate the powertrain of a plug-in fuel cell hybrid vehicle (PHEV). In the proposed ID-HIL system, the in-loop vehicle without a hybrid powertrain can simulate a PHEV’s dynamic characteristics. Meanwhile, the other in-loop subsystems can work in the same way as if they were on board. Thus, the degradation process of the power sources, such as the fuel cells and lithium battery, can be studied in real-world driving conditions. A 21 km on-road driving test proves the ID-HIL’s feasibility and fidelity.

A short-term prediction-based efficient optimization power control strategy for heavy-duty hybrid electric vehicle

This study proposes a short-term prediction-based power control strategy using the modified iteration sequential clustering quadratic programming (MISCQP) algorithm for heavy-duty series hybrid electric vehicles (SHEVs). In this strategy, a power preconditioning method is established on the basis of demand power prediction which guarantees the stable power output under transient high-power condition. Through the prediction sequence, MISCQP algorithm is proposed to solve receding horizon problem and achieve real-time control by improving the iteration efficiency. For this purpose, the clustering algorithm is designed to skip the unnecessary short step in the iteration which is too few to obtain sufficient descent. The iteration points in various iteration domains are clustered and the corresponding cluster centers are obtained. Next, the aforementioned clustering results are introduced to improve the termination criterion. The updated criterion turns to skip the short step when the distance of cluster centers of various iteration domains varies within the set threshold. Finally, the performance of the proposed strategy is validated both in simulation and hardware-in-loop tests. The results reveal that the proposed strategy achieves 5.00 %, 5.86 %, 6.27 % less fuel consumption while maintaining stable power output under all the driving cycles. And the average iteration number of proposed strategy is decreased by 10.36 %, 8.47 %, 9.21 %, respectively.

Optimal design of an adaptive energy management strategy for a fuel cell tractor operating in ports

As World trade is growing rapidly, the reduction of the environmental impact of in-port operations towards a low or zero-emission scenario is becoming a paramount issue. To this aim, replacing Diesel engines of cargo handling equipment used in port logistics (e.g. reach stackers, forklifts, yard tractors, etc.) with cleaner propulsion alternatives may play a major role. In this study, a robust rule-based energy management strategy is proposed for a newly developed fuel cell/battery hybrid powertrain of a yard tractor used for roll-on and roll-off in-port operations. As typical port operations are characterized by mission profiles that can vary significantly in terms of driving and duty cycles during the same work-shift, the proposed strategy, built upon the observation of the powertrain behavior under the application of an optimal controller, dynamically adapts the operation of fuel cell and battery in order to track a predefined battery state of charge trajectory, while minimizing the hydrogen consumption. The use of an optimal model-based approach as a reference for the design of an online implementable energy management strategy is indeed particularly suitable in the present case: despite their inherent high variability, the yard tractor mission profiles can be regarded as the combination of a set of predictable parameters. Results show that the application of the proposed control strategy allows the hybrid powertrain to achieve excellent performance, by leading its components to run efficiently and across suitable operative conditions. The achieved hydrogen consumption, for the considered missions, is only 2%–3% higher than that of the optimal controller, despite a quite different evolution of the battery state of charge, that is the feedback control variable. By means of this strategy, the transient loading of the fuel cell is prevented, while the battery pack ensures the fulfillment of the peak power requests, with beneficial effects in terms of on-board stored energy exploitation. The key advantage of the developed rule-based approach lies in its robustness, reliability and online applicability in real-time powertrain control.

Method for turbocharging and supercharging 2-stroke engines, applied to an opposed-piston new concept for hybrid powertrains

The internal combustion engine's (ICE) electrification calls to question ICE concepts nowadays, leading to new engine architectures conceived to operate as series hybrids. This is the case of the unit analyzed in this work; a 2-stroke, spark-ignited, rod-less opposed-piston engine (2S-ROPE). Previous results revealed some efficiency and maximum power drawbacks, which motivated the development of the supercharged or turbocharged engine version. Supercharging the engine results in a straightforward task; however, turbocharging the engine reveals serious stability problems due to the thermo-fluid-dynamic coupling between the intake and exhaust lines.This work presents a method to study and identify the exhaust line geometry since the impact of pressure pulse propagation on the scavenging process is one of the most critical points to be considered from the very first steps. Finally, despite the difficulties of turbocharging the presented 2S engine, the parametric study revealed a suitable geometrical configuration and the engine operative range. Compared to the supercharged engine version, the turbocharged one presents more difficulties but diminishes the mechanical compressor power consumption, implying an advantage in efficiency terms.

Research on Control Strategy of APSO-Optimized Fuzzy PID for Series Hybrid Tractors

Energy management strategies are crucial for improving fuel economy and reducing the exhaust emissions of hybrid tractors. The authors study a series diesel-electric hybrid tractor (SDEHT) and propose a multi-operating point Fuzzy PID control strategy (MOPFPCS) aimed to achieve better fuel economy and improved control. To further improve the vehicle economy, the adaptive particle swarm optimization method is used to optimize the key parameters of the Fuzzy PID controller. A co-simulation model in AVL-Cruise and Matlab/Simulink environment is developed for plowing mode and transportation mode. The simulation results show that under the two operation modes, the equivalent fuel consumption of the adaptive particle swarm optimization multi-operating points Fuzzy PID control strategy (APSO-MOPFPCS) is reduced by 18.3% and 15.0%, respectively, compared to the engine single-operating point control strategy (ESOPCS). Also, it was found to be reduced by 9.5% and 4.6%, respectively, compared to the MOPFPCS.

An energy saving rule-based strategy for electric-hydraulic hybrid wheel loaders

Recently, hybrid electric vehicles are getting popular as they are both clean and efficient. As one of the hybrid electric powertrains, the battery powered parallel electric-hydraulic hybrid powertrain (PEHHP) is zero emission and has better drive performance. But the energy use of the PEHHP highly depends on the control strategy. A proper energy management strategy is critical for torque distribution and hybrid powertrain efficiency improvement. In this paper, a real-time rule-based strategy is proposed to determine the torque distributions of the PEHHP. The proposed rule-based strategy is based on the component efficiency analysis. And it optimizes the electric motor operating points. Experiments are conducted to show the operation results of the parallel electric-hydraulic hybrid wheel loaders. The powertrain energy use of the proposed rule-based strategy is close to that of the global optimal dynamic programing strategy, with energy use gaps of 2.99%, and 6.10% in simulation and experiment respectively.

Dynamic Simulation Model and Experimental Validation of One Passive Fuel Cell–Battery Hybrid Powertrain for an Electric Light Scooter

Given the escalating issue of climate change, environmental protection is of growing importance. A rising proportion of battery-powered scooters are becoming available. However, their range is limited, and they require a long charging time. The fuel cell–battery-powered electric scooter appears to be a promising alternative. Further development of the active hybrid is the passive hybrid, in which the fuel cell is directly coupled to the battery, eliminating the need for a DC/DC converter. The passive hybrid promises the possibility of a reduction in the installation volume and cost. A simulation model is created MATLAB/Simulink for the passive fuel cell–battery hybrid electric scooter. It specifically focuses on how the power split between the fuel cell and battery occurs under dynamic load requirements. The scooter is powered by two air–hydrogen Proton Exchange Membrane Fuel Cell (PEMFC) systems with a nominal power of 250 W each and a Li-ion battery (48 V, 12 Ah). The validation is performed following an ECE-R47 driving cycle. The maximum relative deviation of the fuel cell is 2.82% for the current value. The results of the simulation show a high level of agreement with the test data. This study provides a method allowing for an efficient assessment of the passive fuel cell–battery hybrid electric scooter.

Linearizing Battery Degradation for Health-Aware Vehicle Energy Management

The utilization of battery energy storage systems (BESS) in vehicle-to-grid (V2G) and plug-in hybrid electric vehicles (PHEVs) benefits the realization of net-zero in the energy-transportation nexus. Since BESS represents a substantial part of vehicle total costs, the mitigation of battery degradation should be factored into energy management strategies. This paper proposes a two-stage BESS aging quantification and health-aware energy management method for reducing vehicle battery aging costs. In the first stage, a battery aging state calibration model is established by analyzing the impact of cycles with various Crates and depth of discharges based on a semi-empirical method. The model is further linearized by learning the mapping relationship between aging features and battery life loss with a linear-in-the-parameter supervised learning method. In the second stage, with the linear battery life loss quantification model, a neural hybrid optimization-based energy management method is developed for mitigating vehicle BESS aging. The battery aging cost function is formulated as a linear combination of system states, which simplifies model solving and reduces computation cost. The case studies in an aggregated EVs peak-shaving scenario and a PHEV with an engine-battery hybrid powertrain demonstrate the effectiveness of the developed method in reducing battery aging costs and improving vehicle total economy. This work provides a practical solution to hedge vehicle battery degradation costs and will further promote decarbonization in the energy-transportation nexus.

Physics-informed neural network-based serial hybrid model capturing the hidden kinetics for sulfur-driven autotrophic denitrification process.

Sulfur-driven autotrophic denitrification (SdAD) is a biological process that can remove nitrate from low carbon/nitrogen (C/N) ratio wastewater. Although this process has been intensively researched, the mechanism whereby its intermediates (i.e., elemental sulfur and nitrite ions) are generated and accumulated remains elusive. Existing mathematical models developed for SdAD cannot accurately predict the intermediates in SdAD because of the incomplete knowledge of process kinetic resulting from changes in the environmental conditions and electron competition during SdAD. To address this limitation, we proposed a novel serial hybrid model structure based on a physics-informed neural network (PINN) to capture the dynamics of the process kinetics and predict the substrate concentrations in SdAD. In this study, we evaluated the model through numerical experiments and applied it to real case studies involving batch and continuous-flow reactor scenarios. By leveraging the PINN approach, the hybrid model yielded accurate predictions at both the state (i.e. substrate concentration) and kinetic levels in the numerical experiments and performed better than both mechanistic and purely data-driven models in the case studies. Furthermore, we used the trained hybrid model to design control strategies for SdAD and a novel integrated process involving SdAD and anammox for energy-efficient nitrogen removal. Finally, we discuss the advantages and application scope of the PINN-based hybrid model.