International Journal of Powertrains (14 papers in press)

Regular Issues

• Coordination Control for Output Voltage of Optical-storage Independent Microgrid based on Adaptive Optimization  
  by Haocheng Zhou, Fengting Lu, Youchun Liu 
  Abstract: The optical-storage independent microgrid system has complex structure, and the change of load parameters will lead to insufficient stability of output voltage. Therefore, a coordination control method for output voltage of optical-storage independent microgrid based on adaptive optimisation is proposed. The topological graph of optical-storage independent microgrid is constructed to obtain the load state parameters of optical-storage independent microgrid for constructing the optimisation model of load state parameters. Through adaptive adjustment of step and square wave response, the load state optimisation parameters are obtained by fusion processing, and then the output voltage of the optical-storage independent microgrid is constrained. The output voltage stability of optical-storage independent microgrid is adjusted, and the coordination control of output voltage stability of optical-storage independent microgrid is realised. The simulation results show that the optical-storage independent microgrid controlled by this method has good output voltage stability and strong adaptive parameter optimisation ability.
  Keywords: microgrid; output voltage; adaptive control; coordination control; load parameters; step response.
  DOI: 10.1504/IJPT.2023.10051099
   
  
• Thermal efficiency predictive modelling of dedicated hybrid engines based on an optimal multi-network structure  
  by Chengqing Wen, Ji Li, Quan Zhou, Guoxiang Lu, Hongming Xu 
  Abstract: This paper presents an evolutionary data-driven modelling approach for dedicated hybrid engine thermal efficiency (TE) prediction, in which a multi-network structure is developed to further improve the prediction accuracy. This structure enables adaptively segmenting input channels in order to reduce the nonlinearity of data representation in each channel so that the sub-networks can be trained efficiently. In the context, the grey wolf optimisation (GWO) algorithm is applied to find the breakpoints of segmentation for building an optimal multi-network structure. The multilayer perceptron (MLP) is introduced as the basic network due to its simple structure with only two hidden layers. Validated by the experimental data, the accuracy of the multi-network prediction model incorporating GWO improves from 82% to 89%. Also, GWO converges to the optimal solution with 21 iterations compared to 26 for particle swarm optimisation and 31 for the gravitational search algorithm, which demonstrates that GWO has a better performance in this study.
  Keywords: optimal network structure; engine thermal efficiency; grey wolf optimisation algorithm; data-driven modelling.
  DOI: 10.1504/IJPT.2024.10056926
   
  
• The matching model of thermal energy supply and demand in power generation park with new energy and municipal solid waste  
  by Jia Zhang, Wei Wu 
  Abstract: This paper focuses on the matching problem of thermal energy supply and demand in the power generation park with new energy and municipal solid waste, and constructs a matching model of thermal energy supply and demand in the power generation park with new energy and municipal solid waste. Firstly, the distributed energy supply base model of supply side is built to analyse the thermal energy supply in the park. Then, the optimal scheduling model of the supply and demand of the park’s thermal energy system. The particle swarm optimisation algorithm with good global searching ability is used as the model solving algorithm. Finally, the effectiveness of the model is verified by a specific example. The results show that the matching scheme of heat energy supply and demand in the park under the economic and environmental protection goals is obtained, so it has good application effect and certain use value.
  Keywords: new energy; municipal solid waste; power generation; park; heat supply and demand; matching model.
  DOI: 10.1504/IJPT.2023.10055385
   
  
• Design of impulse grounding resistance measurement system for distribution network based on wavelet packet optimal algorithm  
  by Qing Wang, Na Yu, Chencong Jin 
  Abstract: Accurate measurement of distribution network grounding resistance is helpful to improve the lightning protection performance of distribution network transmission lines. However, traditional measurement methods can not completely eliminate the invalid interference information in the signal. Therefore, a measurement system for impulse grounding resistance of distribution network based on wavelet packet optimisation algorithm is designed. Compared with traditional systems, this measurement system can more effectively and orderly extracts and process information with wavelet packet optimisation algorithm. The impact current generator is used to generate the impact current. The acquisition board and the main control unit are isolated by V/F transformation module and infrared communication module. In terms of software, the initial resistance signal measurement value is calculated based on the system measurement principle, and the initial resistance signal is processed by the wavelet packet optimal algorithm to remove the interference signal and retain the effective signal reflecting the ground resistivity information. It is verified that the designed system has high measurement precision. It is a highly reliable system for measuring the impulse grounding resistance of distribution networks.
  Keywords: wavelet packet; optimal algorithm; distribution network; impulse current; impulse grounding resistance; resistance signal; measurement system.
  DOI: 10.1504/IJPT.2023.10055386
   
  
• Energy Management of Hybrid Electric Vehicle Based on Linear Time-varying Model Predictive Control  
  by Daofei Li, Jiajie Zhang, Dongdong Jiang 
  Abstract: Energy management of hybrid electric vehicle (HEV) is crucial for improving fuel economy and reducing emissions. Due to the challenges in both development and implementation, sim-plified algorithms of energy management, e.g. rule-based strategies (RB) and equivalent con-sumption minimization strategy (ECMS), still prevail in real vehicle applications. Taking an entry level passenger vehicle with P2 hybrid powertrain for an application example, a bilevel hybrid model predictive control (Bi-HMPC) algorithm is proposed to improve fuel economy of HEV. The upper level calculates the optimal engine/motor torque distribution based on linear time-varying model predictive control (LTV-MPC) algorithm, while the lower level optimizes the gear ratio via hybrid MPC (HMPC). Preliminary simulations demonstrate that the Bi-HMPC has better fuel-saving performances than ECMS and has the potential for practical application. Considering practical difficulties in real vehicle application, the LTV-MPC based torque distri-bution optimization algorithm in the upper level is further implemented in real vehicle valida-tion through dynamometer tests. The initial test, without applying any special limits of engine start or maximum torque, shows that the vehicle fuel consumption is still as high as 7.05L/100km and pollutant emissions are also high. Through several optimizations and im-provements based on expert rules, e.g. optimizing the starting condition of the engine, we can reduce the fuel consumption from 7.05L/100km to 6.2L/100km. Results of real-vehicle experi-ments show that the LTV-MPC algorithm can realize real-time operation on HEV, together with noticeable improvements in fuel economy and pollutant emissions of the tested HEV.
  Keywords: Hybrid Electric Vehicle; Energy Management; Linear Time Varying Model Predictive Control; Fuel Economy; Real Vehicle Tests.
  
  
• Energy Consumption Optimisation for Unmanned Aerial Vehicle based on Reinforcement Learning Framework  
  by Ziyue Wang, Yang Xing 
  Abstract: The average battery life of drones in use today is around 30 minutes, which poses significant limitations for ensuring long-range operation, such as seamless delivery and security monitoring. Meanwhile, the transportation sector is responsible for 93% of all carbon emissions, making it crucial to control energy usage during the operation of UAVs for future net-zero massive-scale air traffic. In this study, a reinforcement learning (RL)-based model was implemented for the energy consumption optimisation of drones. The RL-based energy optimisation framework dynamically tunes vehicle control systems to maximise energy economy while considering mission objectives, ambient circumstances, and system performance. RL was used to create a dynamically optimised vehicle control system that selects the most energy-efficient route. Based on training times, it is reasonable to conclude that a trained UAV saves between 50.1% and 91.6% more energy than an untrained UAV in this study by using the same map.
  Keywords: power consumption; machine learning; reinforcement learning; RL; trajectory optimisation; Q-Learning; energy efficiency; path planning.
  DOI: 10.1504/IJPT.2024.10057473
   
  
• Optimization of Straw Logistics Supply Chain in Biomass Power Generation Project  
  by Jianxin Chen 
  Abstract: Due to the quick growth of biogas power generation project construction, the current straw logistics distribution centres can no longer support the demands of biogas power generation projects due to their limited logistics network coverage and expensive total distribution costs. The optimisation of straw logistics supply chain for biomass power generation projects is proposed. The construction of the straw distribution centres is to integrate the transportation of scattered straw. The supply chain logistics model was selected and the location model of straw supply logistics distribution centre was constructed. For the selection of straw distribution centres, a mixed integer programming model was created with the objective of minimising the overall logistics cost. According to the modelling results, the alternative distribution centres S1 and S2 constructed using this strategy have yearly straw flow rates of 93,500 tonnes and 80,000 tonnes, respectively. A certain amount of practical value can be gained from the average total straw distribution cost of 1.3 million yuan, which can be used to decide on the number of distribution centres, site selection plans, and straw distribution plans. It can also be used to lower the cost of straw supply for projects using straw to generate biogas.
  Keywords: biomass; power generation project; straw logistics; supply chain.
  DOI: 10.1504/IJPT.2023.10057825
   
  
• Construction of SF6 emission estimation model in power equipment  
  by Lijun Zhang, Kecheng Liu, Hesong Han 
  Abstract: Aiming at the problems of low accuracy and long estimation time in traditional methods for estimating sulphur hexafluoride emissions from power equipment, a new model for estimating sulphur hexafluoride emissions from power equipment is established. Online monitoring the decomposition products of sulphur hexafluoride in power equipment by photoacoustic spectroscopy, obtaining the data of the decomposition products of sulphur hexafluoride, establishing a mathematical model of the relationship between the pressure and time of sulphur hexafluoride gas, and detecting the components of the decomposition products of sulphur hexafluoride gas based on the relationship between the pressure and time of sulphur hexafluoride gas, obtain the main decomposition gase, arc, spark discharge and partial discharge, search the emission range of sulphur hexafluoride, and build an estimation model of sulphur hexafluoride emission. The simulation results show that the model has high accuracy and short estimation time for SF6 emissions from power equipment.
  Keywords: power equipment; sulphur hexafluoride; gas emissions; estimation model.
  DOI: 10.1504/IJPT.2023.10059485
   
  

Special Issue on: ICAVP2021 Latest Advancements in Vehicular Powertrain Electrification

• Influence of Valve Spool Shoulder Wall Angle on Steady-state Hydraulic Force  
  by Zi-wei Wang, Shuai Gao, Jian-ren Zhu 
  Abstract: In order to improve the control precision and response speed of electro-hydraulic control system, this paper studies the influence of angle at the shoulder of the main pressure regulating valve spool of a transmission hydraulic system on steady-state hydraulic force of slide valve. The axial hydraulic force on the valve spool with fixed valve opening is calculated by theoretical empirical formula and computer simulation, the results show that the force on the spool is large. To reduce the influence, the steady-state hydraulic force with different shoulder angles is calculated. The results show the law of the hydraulic force when the angle of incident and exit is changed separately.
  Keywords: Main pressure regulating valve; Steady-state hydraulic force; Fluent.
  
  
• Experimental study on effect of torsional vibration attenuation measures for driveline with DCT  
  by Dong Guo, Wenyi Rao, Yi Zhou, Yizhou Xiong, Yi Zhou 
  Abstract: Interior noise and vibration has become one key issue of vehicle with DCT, which has a low gear rattle noise threshold. Four types of torsional vibration attenuation measures, including clutch torsional damper, dual mass flywheel, clutch micro-slip control and centrifugal pendulum vibration absorber are adopted to the DCT driveline. Then road test is conducted and the angular acceleration of the primary flywheel and gearbox input shaft are measured under fourth gear acceleration condition. It is found that the torsional shock absorption performance of the original clutch torsional damper is the worst. The angular acceleration amplitude of the input shaft is greatly reduced by using dual-mass flywheel and clutch micro-slip control, respectively. Dual-mass flywheel with centrifugal pendulum vibration absorbers torsional shock absorption performance is the best?the input shaft angular acceleration is always less than 100 rad/s
  Keywords: Torsional vibration experimental; attenuation measures; clutch micro-sliprncontrol; Centrifugal pendulum.
  
  
• Modelling of a Magnetorheological Fluid Dual Clutch with BP Neural Network  
  by Jin Zhao, Haiping Du, Donghong Ning, Huan Zhang, Lei Deng, Weihua Li 
  Abstract: In this paper, a backpropagation (BP) neural network model for a novel Magneto-rheological fluid dual-clutch (MRFDC) is presented. The MRFDC is a complicated system with high nonlinearity and strong hysteresis, and the conventional parametric modelling methods are based on parameter identification and optimisation. Thus, the modelling work is usually difficult and the performance of conventional models is usually not good enough for the MRFDC. In contrast, the proposed BP neural network model in this work is easily obtained and able to precisely describe the input and output relationship of the MRFDC. To be specific, the proposed BP neural network model approximates the dynamic behaviours of the MRFDC regarding dynamic input currents and rate-dependent hysteresis. The model input variables are selected considering the working mode of the MRFDC and its rate-dependent dynamic magnetic hysteresis. Then, the BP neural network is trained by the input and output data sets obtained from experiments. The model performance is validated by experiments, and experimental results show that the proposed model is able to predict the output torque capacity of the MRFDC precisely with dynamic input currents.
  Keywords: BP Neural Network; Magnetorheological Fluid; Magnetorheological Clutch.
  
  
• A Hybrid Electromechanical Coupling System Optimization  
  by Xuewu Liu, Jiangling Zhao, Zhuochao Liu, Xiangyang Xu, Hongzhong Qi, Yongming Zhu, Peng Dong, Shuhan Wang 
  Abstract: Hybrid electric vehicles can not only save energy and reduce emissions, but also solve the range anxiety problem of electric vehicles, therefore becoming an important breakthrough direction of low-carbon vehicles. The electromechanical coupling system is an important part of hybrid electric vehicles, including the single-motor hybrid system, the series-parallel hybrid system, the power-split hybrid system, and other different routes. First, three basic configurations are established for the three technical routes respectively. Then, the optimal configuration of each basic configuration is obtained through optimization analysis. Finally, the series-parallel technical routes are confirmed by comprehensive comparison, and the configuration design is carried out. This paper systematically describes the whole process of scheme design, which can provide reference for other automobile companies that develop electromechanical coupling systems for hybrid electric vehicles.
  Keywords: hybrid electric vehicles; electromechanical coupling system; basic configuration; optimal configuration; scheme optimization.
  
  
• Gear Condition Monitoring by Augmenting Measured Transmission Error Data for Gear Damage and Propagation Estimation  
  by Stefan Sendlbeck, Shiv Patel, Michael Otto, Karsten Stahl 
  Abstract: Structural changes and damage to gears can lead to critical failure of gear transmission systems. However, current condition monitoring approaches often lack precision due to the limited availability of labelled run-to-failure data. Therefore, we provide an approach to augment measured transmission error data with simulated data. The simulation is based on tactilely measured gear flanks with (micro)pitting of varying severity. We automatically identify (micro)pitting with respect to the gear flank and subsequently simulate geometrical and temporal damage growth. This allows us to compute the temporal change in (micro)pitting expansion, as well as the resulting transmission error. By comparing this simulated with the measured transmission error of the running gear transmission, an estimate of the current degree of gear damage is possible. The presented approach offers to combine state-of-the-art damage propagation models with a dataset of measured gear flanks and data augmentation to determine the health condition of gear transmissions.
  Keywords: Condition Monitoring; Damage Detection; Damage Propagation; Gears; Gear Damage Simulation; Data Augmentation; Transmission Error; Gear Topography; Flank Measurement.
  
  
• Real-time load spectrum analysis for Lifetime Prediction of E-Mobility Drivetrains  
  by Michael Otto, Stefan Sendlbeck, Karsten Stahl 
  Abstract: The drivetrain is a critical subsystem in vehicles, because any failure stops mobility and therefore current drivetrains are designed to be nearly fail-safe despite widely differing operating conditions and drivers of road vehicles. This applies especially to the main gearbox and is a special challenge for e-mobility, where weight reduction is mandatory. Optimization may be possible by using vehicle-specific service intervals based on real driving loads. As a result, lighter gearboxes can be used and a predamage warning and service is only required for demanding drivers or regular high load conditions. As a consecutive effect this may also allow lighter design of the rest of the drivetrain. Therefore, in this manuscript an innovative approach is presented to tackle this challenge by using a novel strategy of combining load spectrum calculation and condition monitoring that adjusts the lacking precision of lifetime prediction.
  Keywords: Transmission; Load spectrum calculation; Condition Monitoring; E-mobility.