International Journal of Powertrains (6 papers in press)

Regular Issues

• Distributed Multiobjective Predictive Control for Connected Electric Vehicles  
  by Yanhong Wu, Zhaofeng Gao, Xiulan Song, Ji Li, Quan Zhou, Hongming Xu 
  Abstract: This paper proposes a distributed multi-objective predictive control (DMPC) strategy to balance the conflicts among driving safety, comfort and economy of vehicular platoon. A vehicular platoon model is established to describe the characteristics of connected electric vehicles. Then, a distributed model predictive controller is developed for vehicular platoon. To reduce the multi-objective conflicts, a weighted-sum-based optimisation method is designed and inserted into the controller. Furthermore, the stability and the iterative feasibility of the proposed strategy is proven. Finally, several experiments are carried out on a self-developed co-Simulink platform with the IPG-Carmaker. Compared with the centralised multi-objective predictive control method, the proposed DMPC strategy distributed multi-objective predictive control method exhibits a 7.69% enhancement in driving safety, a 4.64% improvement in driving comfort and 3.70% advancement in driving economy for platoon.
  Keywords: vehicular platoon; multi-objective conflicts; predictive control; IPG-Carmaker.
  DOI: 10.1504/IJPT.2024.10064787
   
  
• Performance Analysis of Driving Style Identification by using Wolf-Inspired Evolutionary Clustering  
  by Chengqing Wen, Cameron Gorle, Ji Li, Quan Zhou, Hongming Xu 
  Abstract: Driving style identification is an essential task in vehicle technology to improve security, driving experience, and customise targeted energy management strategies. Commonly used clustering algorithms, like K-means plus, occasionally suffer from convergence to local optimum and unreasonable setting of initial centroid placement. This paper proposed a neural wolf-inspired evolutionary clustering method for driving style identification. Driving styles show distinctive features in a time series, thus the fast Fourier transform (FFT) is used to convert time series data into energy data corresponding to the main frequency in the frequency domain. Then grey wolf optimisation (GWO) algorithm as a bio-inspired optimisation algorithm is formulated to search the global optimal clustering centroids. The proposed algorithm is compared with K-means plus, Gaussian mixture model (GMM), and PSO-inspired clustering method to identify three distinct driving styles. The data investigated were collected on a driver-in-the-loop intelligent simulation platform. Silhouette coefficient was selected to evaluate the clustering effect of the test dataset in the clustering model implemented by the proposed algorithm, its five times average reached 0.9531, which is 0.0280 higher than K-means plus, 0.0124 higher than GMM, and 0.0106 higher than PSO-inspired clustering method.
  Keywords: driving style identification; grey wolf optimisation; GWO algorithm; clustering algorithm; fast Fourier transform; FFT; Gaussian mixture model; GMM.
  DOI: 10.1504/IJPT.2024.10065486
   
  
• Establishment and Calibration of Performance Simulation Model for Hydrogen Injector of Fuel Cell  
  by Runing Li, Xing Feng, Zuyong Yang, Jian Zhang 
  Abstract: In order to meet the demand of the PEMFC digital prototype for high-precision simulation models of its key components, based on the analysis of the structure and working principle of the hydrogen injector, the mathematical model of the hydrogen injector was established according to the continuity equation and energy equation of gas flow. On this basis, the one-dimensional performance simulation model of the hydrogen injector was developed using Python computer language; a test bench for nozzle injection characteristics was setup. Compressed air was used to replace hydrogen. The nozzle injection characteristics were tested under different inlet and outlet pressures, and the test data of nozzle gas mass flow were obtained; through the test data and nozzle gas flow model, the orifice flow coefficient was determined to be 0.89, and the one-dimensional performance simulation model of hydrogen injector was calibrated; the accuracy of the one-dimensional performance simulation model of hydrogen injector is verified through various working conditions, and the accuracy of the simulation data can reach 97%, which lays a foundation for the development of the digital prototype of PEMFC.
  Keywords: proton exchange membrane fuel cell; hydrogen injector; nozzle; digital prototype; model calibration.
  DOI: 10.1504/IJPT.2024.10066843
   
  
• Data-Driven Modelling of Battery State-of-Health using Multi-Criteria-Based Feature Reduction  
  by Abdul Azis Abdillah, Cetengfei Zhang, Zhong Ren, Ji Li, Hongming Xu, Quan Zhou 
  Abstract: Previous studies have explored many features that can be used to estimate the health of lithium batteries. However, there are still gaps from previous research, namely, which features should be used and which can be ignored to make the best SOH estimation using machine learning models. This paper proposes a multi-criteria-based feature reduction method to find and combine the best features with machine learning models for estimating the lithium ion battery SOH. This research consists of three main stages: first, determining the features that will be used for building the model, these features include voltage, current, temperature, and time; secondly, carrying out multi-criteria-based feature reduction, existing features are selected based on a combination of four methods such as Pearson correlation rank, Lasso regression, sequential feature selection (SFS), and PCA; third, using the selected features to test the battery health estimation performance using multi-layer perceptrons. The results show that the proposed multi-criteria-based feature reduction method can determine useful features, thereby increasing the generalisation ability and accurate prediction results for lithium-ion battery health degradation under actual EV usage conditions. Besides, the proposed method combined with MLP can outperform other models to 40% of R2.
  Keywords: lithium-ion battery; SOH; Multi-Criteria-Based Feature Reduction; Machine Learning.
  DOI: 10.1504/IJPT.2024.10067235
   
  
• Research on Flexible and Controllable Starting Strategy for Commercial Vehicle Diesel Range Extender  
  by Zhuo Zhang, Yuzhen Yuan, Zhiqiang Lin, Kang Song, Zhengsong Mao, Fahua Huang 
  Abstract: For range extender, engine starting conditions and boosting methods lead to varying oil film formation and pressure establishment durations, resulting in differences in optimal starting durations.This paper primarily addresses the control issue of range extender starting duration.Initially, it establishes and validates the dynamic model of the target range extender system, consisting of a four-cylinder diesel engine and a flywheel integrated starter generator(FISG).Subsequently, designing an adjustable and controllable target speed curve by polynomial splicing ,and calibrates curve parameters by optimizing vibration performance indicators. Based on the target speed curve, the motor torque is designed, with the torque corresponding to the speed primarily constructing the feedforward torque, supplemented by the feedback torque.Finally, the proposed starting control strategy is validated on the test bench, compared with the original strategy, and the impact of different control parameters on the starting control effect is analyzed, confirming the effectiveness of the proposed strategy in achieving flexible and controllable starting times.
  Keywords: hybrid electric vehicles; range extender,start processes.
  DOI: 10.1504/IJPT.2024.10068004
   
  
• Energy Management Improvement in the Charge-Sustaining Mode of a Plug-In Hybrid Electric Vehicle  
  by Kangjie Liu, Boyang Yu, Yongzheng Sun, Jiyun Xu, Yiqiang Liu, Zhiyu Han 
  Abstract: This study investigates a locally optimized dynamic equivalent consumption minimization strategy (DECMS) and its engineering-improved variant, EI-DECMS, alongside an engineering rule-based switching power following and multi-point strategy (SPM) and its enhanced version, I-SPM, for plug-in hybrid electric vehicles (PHEVs). The EI-DECMS aims to reduce engine start-stop frequency, enhancing vehicle NVH performance. Focusing on the charge-sustaining mode (or range-extension mode), these four strategies were evaluated through road tests and chassis dynamometer experiments. The results indicate that the DECMS strategy not only automates optimisation but also outperforms SPM in fuel economy, achieving reductions of 2.8% to 7.44% in real-world tests and a 13.79% decrease in dynamometer experiments. Additionally, the EI-DECMS strategy results in fewer engine start-stop occurrences and improved state-of-charge balancing compared to both DECMS and I-SPM. Conversely, the I-SPM strategy, after extensive calibration, delivered fuel consumption comparable to that of EI-DECMS, underscoring DECMS's advantage in automated optimisation.
  Keywords: plug-in hybrid electric vehicles; range extension; fuel economy; energy management strategy; dynamic equivalent consumption minimization strategy.
  DOI: 10.1504/IJPT.2025.10068011