Browsing by Author "Li, Liang"

Now showing 1 - 4 of 4
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    Cooperative ecological adaptive cruise control for plug-in hybrid electric vehicle based on approximate dynamic programming
    (IEEE, 2022-10-26) Li, Jie; Liu, Yonggang; Fotouhi, Abbas; Wang, Xiangyu; Chen, Zheng; Zhang, Yuanjian; Li, Liang
    Eco-driving control generates significant energy-saving potential in car-following scenarios. However, the influence of preceding vehicle may impose unnecessary velocity waves and deteriorate fuel economy. In this research, a learning-based method is exploited to achieve satisfied fuel economy for connected plug-in hybrid electric vehicles (PHEVs) with the advantage of vehicle-to-vehicle communication system. A data-driven energy consumption model is leveraged to generate reinforcement signals for approximate dynamic programming (ADP) with the consideration of nonlinear efficiency characteristics of hybrid powertrain system. An advanced ADP scheme is designed for connected PHEVs driving in car-following scenarios. Additionally, the cooperative information is incorporated to further improve the fuel economy of the vehicle under the premise of driving safety. The proposed method is mode-free and showcases acceptable computational efficiency as well as adaptability. The simulation results demonstrate that the fuel economy during car-following processes is remarkably improved through cooperative driving information, thereby partially paving the theoretical basis for energy-saving transportation.
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    An innovative information fusion method with adaptive Kalman filter for integrated INS/GPS navigation of autonomous vehicles
    (Elsevier, 2017-09-04) Liu, Yahui; Fan, Xiaoqian; Lv, Chen; Wu, Jian; Li, Liang; Ding, Dawei
    Information fusion method of INS/GPS navigation system based on filtering technology is a research focus at present. In order to improve the precision of navigation information, a navigation technology based on Adaptive Kalman Filter with attenuation factor is proposed to restrain noise in this paper. The algorithm continuously updates the measurement noise variance and processes noise variance of the system by collecting the estimated and measured values, and this method can suppress white noise. Because a measured value closer to the current time would more accurately reflect the characteristics of the noise, an attenuation factor is introduced to increase the weight of the current value, in order to deal with the noise variance caused by environment disturbance. To validate the effectiveness of the proposed algorithm, a series of road tests are carried out in urban environment. The GPS and IMU data of the experiments were collected and processed by dSPACE and MATLAB/Simulink. Based on the test results, the accuracy of the proposed algorithm is 20% higher than that of a traditional Adaptive Kalman Filter. It also shows that the precision of the integrated navigation can be improved due to the reduction of the influence of environment noise.
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    Multi-response optimisation of machining aluminium-6061 under eco-friendly electrostatic minimum quantity lubrication environment
    (Inderscience, 2019-10-09) Jamil, Muhammad; Khan, Aqib Mashood; He, Ning; Li, Liang; Zhao, Wei; Sarfraz, Shoaib
    The emerging grave consequences of conventional coolants on health, ecology and product quality, have pushed the scientific research to explore eco-friendly lubrication technique. Electrostatic minimum quantity lubrication (EMQL) has been underscored as a burgeoning technology to cut-down bete noire impacts in machining. This research confers the adoption of a negatively charged cold mist of air-castor oil employed in turning of aluminium-6061T6 material by varying the cutting conditions, as per experimental designed through response surface methodology (RSM). For comprehensive sagacity, a range of cutting speed, feed, depth of cut and EMQL-flow rate were considered. Material removal rate, tool life, surface roughness and power consumption of machine tool were adopted as performance measures. To satisfy multi-criterion simultaneously, RSM-based grey relational analysis (GRA) was employed for multi-objective optimisation. Highest proportion of grey relational grade (GRG) as a single desideratum response function, provided a trade-off between performance measures with 15.56% improvement in GRG.
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    Taguchi-based GRA for parametric optimization in turning of AISI L6 tool steel under cryogenic cooling
    (IOS Press, 2019-09-12) ul Haq, Emran; Li, Liang; Jamil, Muhammad; Khan, Aqib Mashood; Sarfraz, Shoaib; Shehab, Essam
    Cutting fluids have frequent use in industrial sector to improve the machinability. Due to the negative impact on our ecology, recent focus has shifted to explore some environment-friendly cooling techniques such as cryogenic cooling. Cryogenic cooling involving liquid nitrogen is one of the alternative techniques which improves the efficiency of the machining process and is environmentally friendly as well. In current work, cutting parameters in turning such as cutting speed and feed rate were optimized under cryogenic cooling for machining of AISI L6 tool steel which is difficult to cut material. The output parameters under consideration are surface roughness, cutting energy, tool wear and Material Removal Rate (MRR). The optimization for multi-responses was carried out through Taguchi based Grey Relational Analysis (GRA). For experimental design, tests were based on L9 orthogonal array. According to the GRA optimization results, optimum cutting speed level was 160 m/min and the feed rate was 0.16 mm/rev. The percentage improvement in Grey Relational Grade (GRG) was calculated as 19.07%, thus showing the advantage of using the GRA.