Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution

[EN] In this paper, we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles (HEVs) on a road with slope. Moreover, it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything (V2X), including geographic information, vehicle-to-infrastructure (V2I) information and vehicle-to-vehicle (V2V) information. The provided simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator, where the running route is generated based on real-world data with slope and intersection position. The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time. 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Estimation of fuel consumption in a hybrid electric refuse collector vehicle using a real drive cycle

Postprint (published version

Modelling driving behaviour and its impact on the energy management problem in hybrid electric vehicles

Perfect knowledge of future driving conditions can be rarely assumed on real applications when optimally splitting power demands among different energy sources in a hybrid electric vehicle. Since performance of a control strategy in terms of fuel economy and pollutant emissions is strongly affected by vehicle power requirements, accurate predictions of future driving conditions are needed. This paper proposes different methods to model driving patterns with a stochastic approach. All the addressed methods are based on the statistical analysis of previous driving patterns to predict future driving conditions, some of them employing standard vehicle sensors, while others require non-conventional sensors (for instance, global positioning system or inertial reference system). The different modelling techniques to estimate future driving conditions are evaluated with real driving data and optimal control methods, trading off model complexity with performance.Guardiola García, C.; Plá Moreno, B.; Blanco Rodriguez, D.; Reig Bernad, A. (2014). Modelling driving behaviour and its impact on the energy management problem in hybrid electric vehicles. International Journal of Computer Mathematics. 91(1):147-156. doi:10.1080/00207160.2013.829567S147156911Ericsson, E. (2001). Independent driving pattern factors and their influence on fuel-use and exhaust emission factors. Transportation Research Part D: Transport and Environment, 6(5), 325-345. doi:10.1016/s1361-9209(01)00003-7Q. Gong, P. Tulpule, V. Marano, S. Midlam-Mohler, and G. Rizzoni,The role of ITS in PHEV performance improvement, 2011 American Control Conference, June–July, San Francisco, CA, 2011, pp. 2119–2124.C. Guardiola, B. Pla, S. Onori, and G. Rizzoni,A new approach to optimally tune the control strategy for hybrid vehicles applications, IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling E-COSM’12, October, Rueil-Malmaison, France, 2012.Johannesson, L., Asbogard, M., & Egardt, B. (2007). Assessing the Potential of Predictive Control for Hybrid Vehicle Powertrains Using Stochastic Dynamic Programming. IEEE Transactions on Intelligent Transportation Systems, 8(1), 71-83. doi:10.1109/tits.2006.884887Liu, S., & Yao, B. (2008). Coordinate Control of Energy Saving Programmable Valves. IEEE Transactions on Control Systems Technology, 16(1), 34-45. doi:10.1109/tcst.2007.903073Paganelli, G. (2001). General supervisory control policy for the energy optimization of charge-sustaining hybrid electric vehicles. JSAE Review, 22(4), 511-518. doi:10.1016/s0389-4304(01)00138-2Rizzoni, G., Guzzella, L., & Baumann, B. M. (1999). Unified modeling of hybrid electric vehicle drivetrains. IEEE/ASME Transactions on Mechatronics, 4(3), 246-257. doi:10.1109/3516.789683Control of hybrid electric vehicles. (2007). IEEE Control Systems, 27(2), 60-70. doi:10.1109/mcs.2007.338280L. Serrao, S. Onori, and G. Rizzoni,ECMS as realization of Pontryagin's minimum principle for HEV control, 2009 American Control Conference, June, Saint Louis, MO, 2009, pp. 3964–3969.Serrao, L., Onori, S., & Rizzoni, G. (2011). A Comparative Analysis of Energy Management Strategies for Hybrid Electric Vehicles. Journal of Dynamic Systems, Measurement, and Control, 133(3). doi:10.1115/1.4003267Stockar, S., Marano, V., Canova, M., Rizzoni, G., & Guzzella, L. (2011). Energy-Optimal Control of Plug-in Hybrid Electric Vehicles for Real-World Driving Cycles. IEEE Transactions on Vehicular Technology, 60(7), 2949-2962. doi:10.1109/tvt.2011.2158565Sundström, O., Ambühl, D., & Guzzella, L. (2009). On Implementation of Dynamic Programming for Optimal Control Problems with Final State Constraints. Oil & Gas Science and Technology – Revue de l’Institut Français du Pétrole, 65(1), 91-102. doi:10.2516/ogst/2009020O. Sundström and L. Guzzella,A generic dynamic programming Matlab function, 18th IEEE International Conference on Control Applications Part of 2009 IEEE Multi-conference on Systems and Control, July, Saint Petersburg, 2009, pp. 1625–1630.R. Wang and S.M. Lukic,Review of driving conditions prediction and driving style recognition based control algorithms for hybrid electric vehicles, Vehicle Power and Propulsion Conference (VPPC), 2011 IEEE, September 6–9, Raleigh, NC, 2011, pp. 1–7

A state-of-the-art review on torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains

© 2019, Levrotto and Bella. All rights reserved. Electric vehicles are the future of private passenger transportation. However, there are still several technological barriers that hinder the large scale adoption of electric vehicles. In particular, their limited autonomy motivates studies on methods for improving the energy efficiency of electric vehicles so as to make them more attractive to the market. This paper provides a concise review on the current state-of-the-art of torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains (FEVIADs). Starting from the operating principles, which include the "control allocation" problem, the peculiarities of each proposed solution are illustrated. All the existing techniques are categorized based on a selection of parameters deemed relevant to provide a comprehensive overview and understanding of the topic. Finally, future concerns and research perspectives for FEVIAD are discussed

Exclusive Operation Strategy for the Supervisory Control of Series Hybrid Electric Vehicles

Supervisory control systems (SCSs) are used to manage the powertrain of hybrid electric vehicles (HEV). This paper presents a novel SCS called Exclusive operation strategy (XOS) that applies simple rules based on the idea that batteries are efficient at lower loads while engines and generators are efficient at higher loads. The XOS is developed based on insights gained from three conventional SCSs for series HEVs: Thermostat control strategy (TCS), Power follower control strategy (PFCS) and Global equivalent consumption minimization strategy (GECMS). Also, recent technological developments have been considered to make the XOS more suited to modern HEVs than conventional SCSs. The resulting control decisions are shown to emulate the operation of approximate global optimal solutions and thus achieve significant improvement in fuel economy as compared to TCS and PFCS. In addition, the generally linear relationship between required power and engine power for the XOS provides auditory cues to the driver that are comparable to conventional vehicles, thus reducing barriers to adopting HEVs. The simplicity and effectiveness of the XOS makes it a practical SCS

Time-optimal Control Strategies for Electric Race Cars with Different Transmission Technologies

This paper presents models and optimization methods to rapidly compute the achievable lap time of a race car equipped with a battery electric powertrain. Specifically, we first derive a quasi-convex model of the electric powertrain, including the battery, the electric machine, and two transmission technologies: a single-speed fixed gear and a continuously variable transmission (CVT). Second, assuming an expert driver, we formulate the time-optimal control problem for a given driving path and solve it using an iterative convex optimization algorithm. Finally, we showcase our framework by comparing the performance achievable with a single-speed transmission and a CVT on the Le Mans track. Our results show that a CVT can balance its lower efficiency and higher weight with a higher-efficiency and more aggressive motor operation, and significantly outperform a fixed single-gear transmission.Comment: 5 pages, 4 figures, submitted to the 2020 IEEE Vehicle Power and Propulsion Conferenc