A data-driven framework for forecasting transient vehicle thermal performances

References

• X. Qi, G. Wu, P. Hao, K. Boriboonsomsin and M. J. Barth, “Integrated-connected eco-driving system for phevs with co-optimization of vehicle dynamics and powertrain operations,” IEEE Trans. Intell. Veh., vol. 2, no. 1, pp. 2–13, 2017. DOI: 10.1109/TIV.2017.2708599.
   Google Scholar
• A. Ferrara, G. P. Incremona and E. Regolin, Incremona, “Optimization‐based adaptive sliding mode control with application to vehicle dynamics control,” Int. J. Robust Nonlinear Control., vol. 29, no. 3, pp. 550–564, 2019. DOI: 10.1002/rnc.4105.
   Web of Science ®Google Scholar
• A. Brandt, B. Jacobson and S. Sebben, “High speed driving stability of road vehicles under crosswinds: an aerodynamic and vehicle dynamic parametric sensitivity analysis,” Veh. Syst. Dyn., vol. 60, no. 7, pp. 2334–2357, 2022. DOI: 10.1080/00423114.2021.1903516.
   Web of Science ®Google Scholar
• E. A. Castañeda, R. P. León and J. Cornejo, “FEM and DEM simulations of tire-soil and drill-soil interactions in off-road conditions for mechanical design validation of a space exploration rover,” 12th International Conference on Mechanical and Aerospace Engineering (ICMAE), Athens, Greece: IEEE, Jul. 16, 2021. DOI: 10.1109/ICMAE52228.2021.9522493
   Google Scholar
• E. Joa, K. Yi and Y. Hyun, “Estimation of the tire slip angle under various road conditions without tire–road information for vehicle stability control,” Control Eng. Pract, vol. 86, pp. 129–143, 2019. DOI: 10.1016/j.conengprac.2019.03.005.
   Web of Science ®Google Scholar
• S. Khaleghian, O. Ghasemalizadeh, S. Taheri and G. Flintsch, “A combination of intelligent tire and vehicle dynamic based algorithm to estimate the tire-road friction,” SAE Int. J. Passeng. Cars Mech, vol. 12, no. 2, pp. 81–98, 2019. DOI: 10.4271/06-12-02-0007.
   Web of Science ®Google Scholar
• M. Alcázar Vargas, J. Pérez Fernández, I. Sánchez Andrades, J. A. Cabrera Carrillo and J. J. Castillo Aguilar, “Modeling of the influence of operational parameters on tire lateral dynamics,” Sens., vol. 22, no. 17, pp. 6380, 2022. DOI: 10.3390/s22176380.
   PubMed Web of Science ®Google Scholar
• J. A. Hernandez, I. Al-Qadi and M. De Beer, “Impact of tire loading and tire pressure on measured 3D contact stresses,” Airfield & Highway Pavement Conference, 2013. DOI: 10.1061/9780784413005.044
   Google Scholar
• L. Teodosio, F. Timpone, G. N. dell’Annunziata and A. Genovese, “RANS 3D CFD simulations to enhance the thermal prediction of tyre thermodynamic model: A hierarchical approach,” Results Eng., vol. 12, pp. 100288, 2021. DOI: 10.1016/j.rineng.2021.100288.
   Google Scholar
• X. Fu, Z. Wang, L. Ma, Z. Zou, Q. Zhang and X. Guan, “Temperature-Dependence of rubber hyperelasticity based on the eight-chain model,” Polymers, vol. 12, no. 4, pp. 932, 2020. DOI: 10.3390/polym12040932.
   PubMed Web of Science ®Google Scholar
• A. Sakhnevych, et al., “Investigation on the model-based control performance in vehicle safety critical scenarios with varying tyre limits,” Sensors, vol. 21, no. 16, pp. 5372, 2021. DOI: 10.3390/s21165372.
   PubMed Web of Science ®Google Scholar
• K. Piotrowska, et al., “Assessment of the environmental impact of a car tire throughout its lifecycle using the LCA method,” Materials, vol. 12, no. 24, pp. 4177, 2019. DOI: 10.3390/ma12244177.
   PubMed Web of Science ®Google Scholar
• K. Grigoriadis, G. Mavros, J. Knowles and A. Pezouvanis, “Experimental investigation of tyre-road friction considering topographical roughness variation and flash temperature,” Tribol. Int., vol. 181, pp. 108294, 2023. DOI: 10.1016/j.triboint.2023.108294.
   Web of Science ®Google Scholar
• Y. Li, S. Zuo, L. Lei, X. Yang and X. Wu, “Analysis of impact factors of tire wear,” J. Vib. Control., vol. 18, no. 6, pp. 833–840, 2012. DOI: 10.1177/1077546311411756.
   Web of Science ®Google Scholar
• J. Wu, L. Chen, Y. Wang, B. Su, Z. Cui and D. Wang, “Effect of temperature on wear performance of aircraft tire tread rubber,” Polym. Test, vol. 79, pp. 106037, 2019. DOI: 10.1016/j.polymertesting.2019.106037.
   Web of Science ®Google Scholar
• G. Fu and A. Untaroiu, “Investigation of tire rotating modeling techniques using computational fluid dynamics,” J. Fluids Eng., vol. 143, no. 11, pp.111206, 2021. DOI: 10.1115/1.4051311.
   PubMed Web of Science ®Google Scholar
• J. A. Cabrera, J. J. Castillo, J. Pérez, J. M. Velasco, A. J. Guerra and P. Hernández, “A procedure for determining tire-road friction characteristics using a modification of the magic formula based on experimental results,” Sensors, vol. 18, no. 3, pp. 896, 2018. DOI: 10.3390/s18030896.
   PubMed Web of Science ®Google Scholar
• Lugaro, I. Konstantinou and M. Mazzeo, “A tire model extension for predicting temperature and rolling speed influences on tire performance,” JSAE Annu. Congr., vol. 5, pp. 25-26, 2020.
   Google Scholar
• N. J. Persson, “Theory of rubber friction and contact mechanics,” J. Chem. Phys, vol. 115, no. 8, pp. 3840–3861, 2001. DOI: 10.1063/1.1388626.
   Web of Science ®Google Scholar
• M. Furlan and G. Mavros, “A Neural Network Approach for Roughness-Dependent Update of Tyre Friction,” Simul. Model. Pract. Theory, vol. 116, pp. 102484, 2022. DOI: 10.1016/j.simpat.2021.102484.
   Web of Science ®Google Scholar
• T. S. Song, J. W. Lee and H. J. Yu, “Rolling resistance of tires-an analysis of heat generation,” SAE Trans., vol. 107, pp. 507–511, 1998. DOI: 10.4271/980255.
   Google Scholar
• W. V. Mars and J. R. Luchini, “An analytical model for the transient rolling resistance behavior of tires,” Tire. Sci. Technol., vol. 27, no. 3, pp. 161–175, 1999. DOI: 10.2346/1.2135982.
   Google Scholar
• J. R. Cho, H. W. Lee, W. B. Jeong, K. M. Jeong and K. W. Kim, “Numerical estimation of rolling resistance and temperature distribution of 3-D periodic patterned tire,” Int. J. Solids Struct., vol. 50, no. 1, pp. 86–96, 2013. DOI: 10.1016/j.ijsolstr.2012.09.004.
   Web of Science ®Google Scholar
• H. Pacejka, Tire and Vehicle Dynamics, 3rd ed. San Francisco, CA, USA: Morgan Kaufmann, 2012.
   Google Scholar
• H. B. Pacejka and I. J. M. Besselink, “Magic formula tyre model with transient properties,” Veh. Syst. Dyn., vol. 27, no. sup001, pp. 234–249, 1997. DOI: 10.1080/00423119608969658.
   Web of Science ®Google Scholar
• H. B. Pacejka and E. Bakker, “The magic formula tyre model,” Veh. Syst. Dyn., vol. 21, no. sup001, pp. 1–18, 1992. DOI: 10.1080/00423110408969994.
   Google Scholar
• R. J. Martinez, “Flat-Trac® tire force & moment measurement systems,” Eden Prairie, MN, USA: MTS Systems Corporation. [Online]. Available: https://www.mts.com/en/products/automotive/tire-test-systems/flat-trac-tire-system. Accessed: Oct. 24, 2022.
   Google Scholar
• C. Angrick, S. van Putten and G. Prokop, “Influence of tire core and surface temperature on lateral tire characteristics,” SAE Int. J. Passeng. Cars Mech. Syst., vol. 7, no. 2, pp. 468–481, 2014. DOI: 10.4271/2014-01-0074.
   Google Scholar
• J. M. Besselink, A. J. Schmeitz and H. B. Pacejka, “An improved Magic Formula/Swift tyre model that can handle inflation pressure changes,” Veh. Syst. Dyn., vol. 48, no. sup1, pp. 337–352, 2010. DOI: 10.1080/00423111003748088.
   Web of Science ®Google Scholar
• M. Furlan, “Mfeval (version 4.3.1),” MATLAB central file exchange,” [Online]. Available: https://www.mathworks.com/matlabcentral/fileexchange/63618-mfeval. Accessed: Mar. 12, 2021.
   Google Scholar
• Y. Hua, Z. Zhao, R. Li, X. Chen, Z. Liu and H. Zhang, “Deep learning with long short-term memory for time series prediction,” IEEE Commun. Mag., vol. 57, no. 6, pp. 114–119, 2019. DOI: 10.1109/MCOM.2019.1800155.
   Web of Science ®Google Scholar
• X. Zhang, X. Liang, A. Zhiyuli, S. Zhang, R. Xu and B. Wu, “AT-LSTM: An attention-based LSTM model for financial time series prediction,” IOP Conf. Ser. Mater. Sci. Eng., vol. 569, no. 5, pp. 052037, 2019. DOI: 10.1088/1757-899X/569/5/052037.
   Google Scholar
• J. Guo, Z. Lao, M. Hou, C. Li and S. Zhang, “Mechanical fault time series prediction by using EFMSAE-LSTM neural network,” Measurement, vol. 173, pp. 108566, 2021. DOI: 10.1016/j.measurement.2020.108566.
   Web of Science ®Google Scholar
• H. Xie, L. Zhang and C. P. Lim, “Evolving CNN-LSTM models for time series prediction using enhanced grey wolf optimizer,” IEEE Access, vol. 8, pp. 161519–161541, 2020. DOI: 10.1109/ACCESS.2020.3021527.
   Web of Science ®Google Scholar
• J. Wang, W. Jiang, Z. Li and Y. Lu, “A new multi-scale sliding window LSTM framework (MSSW-LSTM): A case study for GNSS time-series prediction,” Remote Sens., vol. 13, no. 16, pp. 3328, 2021. DOI: 10.3390/rs13163328.
   Google Scholar
• S. Karasu and A. Altan, “Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization,” Energy, vol. 242, pp. 122964, 2022. DOI: 10.1016/j.energy.2021.122964.
   Web of Science ®Google Scholar
• R. K. Pathan, M. Biswas and M. U. Khandaker, “Time series prediction of COVID-19 by mutation rate analysis using recurrent neural network-based LSTM model,” Chaos Solitons Fractals, vol. 138, pp. 110018, 2020. DOI: 10.1016/j.chaos.2020.110018.
   PubMed Web of Science ®Google Scholar
• L. Chen, Y. Chi, Y. Guan and J. Fan, “A hybrid attention-based EMD-LSTM model for financial time series prediction,” in 2019 2nd international conference on artificial intelligence and big data (ICAIBD), Chengdu, China: IEEE, May 25, 2019, pp. 113-118. DOI: 10.1109/icaibd.2019.8837038.
   Google Scholar
• Q. Zhaowei, L. Haitao, L. Zhihui and Z. Tao, “Short-term traffic flow forecasting method with MB-LSTM hybrid network,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 1, pp. 225-235, 2020. DOI: 10.1109/tits.2020.3009725.
   Google Scholar
• S. Shin, S. Lee, M. Kim, J. Park and K. Min, “Deep learning procedure for knock, performance and emission prediction at steady-state condition of a gasoline engine,” Proc. Inst. Mech. Eng. D J. Automob. Eng., vol. 234, no. 14, pp. 3347–3361, 2020. DOI: 10.1177/0954407020932690.
   Web of Science ®Google Scholar
• S. Saraeian, B. Shirazi and H. Motameni, “Optimal autonomous architecture for uncertain processes management,” Inf. Sci., vol. 501, pp. 84–99, 2019. DOI: 10.1016/j.ins.2019.05.095.
   Web of Science ®Google Scholar
• S. Bhandari, “Landing a spaceship with Koopman operator theory and model predictive control,” Master thesis, Dept. Inform, TU Munich., München, DE, 2021.
   Google Scholar
• L. Xiong, W. Han and Z. Yu, “Adaptive sliding mode pressure control for an electro-hydraulic brake system via desired-state and integral-antiwindup compensation,” Mechatronics, vol. 68, pp. 102359, 2020. DOI: 10.1016/j.mechatronics.2020.102359.
   Web of Science ®Google Scholar