Abstract
In this study, the electric bike is investigated under the data-driven model predictive control (MPC) model approach to develop road disturbance estimation. The focus of the research study is to develop a new control algorithm proposal for cargo e-bikes subjecting to uncertain road conditions in urban transportation. The developed model is proposed for model predictive driving option in e-bikes to provide optimal motor torque operation against predicted road disturbances. The driving profile is designed to simulate urban driving in different road types by considering pedal usage frequency and pedal load measurement in combined drive. Firstly, a datalogger is designed, and the microcontroller is programmed to measure pedal force independently in all road profiles. Road profiles are divided into two groups, pedal-assisted routes and the full electric driving mode, and all range capacities are recorded to make allover comparisons. Road types consist of asphalt and graded gravel at different slope levels. Pedal load statistical relationship based on each route is also indicated, and the road-based difference is also presented. The obtained data are used for data-driven model development in the MPC control model as measured disturbances. The control model then is integrated into the one-dimensional model to provide road estimation. The design of the developed model provides sensorless road disturbance estimation in urban driving. The proposed control model presents an innovative approach for system designing of micro-e-mobility vehicles in optimum longitudinal vehicle control considering the data-driven MPC method.
Similar content being viewed by others
References
Serra GF, Fernandes FAO, Noronha E, Sousa, Alves de Sousa RJ (2021) Head protection in electric micromobility: a critical review, recommendations, and future trends. Accid Anal Prev 163:106430
Kamran M, Raugei M, Hutchinson A (2021) A dynamic material flow analysis of lithium-ion battery metals for electric vehicles and grid storage in the UK: assessing the impact of shared mobility and end-of-life strategies. Resour Conserv Recycl 167:105412
Kim S, Lee U, Lee I, Kang N (2022) Idle vehicle relocation strategy through deep learning for shared autonomous electric vehicle system optimization. J Clean Prod 333:130055
Reddy AKVK, Narayana KVL (2022) Meta-heuristics optimization in electric vehicles—an extensive review. Renew Sustain Energy Rev 160:112285
Zhang S, Tak T (2021) Efficiency evaluation of electric bicycle power transmission systems. Sustainability 13:10988. https://doi.org/10.3390/su131910988
Masti D, Bemporad A (2018) Learning nonlinear state-space models using deep autoencoders. In: IEEE conference on decision and control (CDC), Miami, USA, pp 3862–3867
Zamarreno JM, Vega P, Garcia LD, Francisco M (2000) State-space neural network for modelling, prediction and control. Control Eng Pract 8:1063–1075
Yao Q, Tian Y (2019) A model predictive controller with longitudinal speed compensation for autonomous vehicle path tracking. Appl Sci 9:4739. https://doi.org/10.3390/app9224739
Ho PJ, Yi CP, Lin YJ, Chung WD, Chou PH, Yang SC (2023) Torque measurement and control for electric-assisted bike considering different external load conditions. Sensors 23:4657. https://doi.org/10.3390/s23104657
Chang SB, Chen PC, Chuang HS, Hsiao CC (2012) Velocity control with disturbance observer for pedal-assisted electric bikes. Veh Syst Dyn 50(11):1631–1651
Mattsson M, Mehler R, Jonasson M, Thomasson A (2016) Optimal model predictive acceleration controller for a combustion engine and friction brake actuated vehicle. In: 8th IFAC international symposium on advances in automotive control, Sweden, pp 521–528
Tadeparti S, Devika KB, Subramanian SC (2023) Computationally efficient non-linear model predictive control for truck platoons. In: 2023 Europen control conference (ECC), Romania
Buechel M, Knoll A (2016) A parameter estimator for a model based adaptive control scheme for longitudional control of automated vehicles. IFAC Pap 49(15):181–186
Rokonuzzaman M, Mohajer N, Nahavandi S, Mohamed S (2021) Model predictive control with learned vehicle dynamics for autonomous vehicle path tracking. IEEE Access 9:128233–128249. https://doi.org/10.1109/ACCESS.2021.3112560
Maceira D, Luaces A, Lugris U, Naya MA, Sanjurjo E (2021) Roll angle estimation of a motorcycle through inertial measurements. Sensors 21:6626. https://doi.org/10.3390/s21196626
Chen CK, Chu TD (2015) Modeling and model predictive control for a bicycle-rider system. In: 2nd international conference on information science and control engineering. IEEE, pp 810–814
Chen CK, Dao TK (2010) A study of bicycle dynamics via system identification. J Chin Inst Eng 35(7):853–868. https://doi.org/10.1080/02533839.2012.708533
Tatjewski P (2017) Offset-free nonlinear model predictive control with state-space process models. Arch Control Sci 27(4):595–615
Faanes A, Skogestad S (2003) State-space realization of model predictive controllers without active constraints. Model Identif Control 24(4):231–244
Meyer D, Kloss G, Senner V (2016) What is slowing me down? Estimation of rolling resistances during cycling. Procedia Eng 147:526–531
Acknowledgements
This study is funded and supported by Bursa Technical University Scientific Research Projects Committee with Project Number 221N015 titled ‘Generating Urban Driving Profiles and Determination of Test Procedures for Determining Energy Efficiency of Electric Bicycle Powertrain System,’ and TUBITAK 2219 Project titled ‘An Experimental Battery Model Development for PAS (Pedal Assist System) of E-Quadricycle under Hill Hybrid Driving Modes at Different Ambient Temperatures.’
Author information
Authors and Affiliations
Corresponding author
Additional information
Technical Editor: Adriano Almeida Gonçalves Siqueira.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Genç, M.O. Data-driven model predictive control using road-based disturbance estimations in longitudinal driving of e-bike. J Braz. Soc. Mech. Sci. Eng. 46, 204 (2024). https://doi.org/10.1007/s40430-024-04778-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s40430-024-04778-1