Optimal Control for Energy Management of Connected Hybrid Electrical Vehicles Predictive Connectivity Compared to an Adaptive Algorithm Hamza Idrissi Hassani Azami 1,2 , Stephane Caux 1 , Frederic Messine 1 and Mariano Sans 3 1 University of Toulouse, LAPLACE, INPT, 2 rue Charles Camichel B.P. 7122, 31071, Toulouse, France 2 French Environment and Energy Management Agency (ADEME), 20 avenue du Gresille-BP 90406, 49004, Angers Cedex 01, France 3 Continental Automotive SaS, 1 avenue Paul Ourliac, 31100, Toulouse, France Keywords: Energy Management, Hybrid Electrical Vehicle, Optimal Control, Pontryagin Minimum Principle, Shooting Algorithms. Abstract: For fuel consumption and CO 2 emissions reduction, an optimal predictive control strategy for connected hybrid electrical vehicles is proposed, and evaluated through a comparison to an adaptive strategy. The predictive strategy relies on the future driving conditions that can be predicted by intelligent navigation systems with real- time connectivity. The theory proposed for such real-time optimal predictive algorithm is based on Pontryagin minimum principle, a mathematical principle that provides general solutions for dynamic systems optimization with integral criteria, under given constraints. In this work, the energy management problem is mathematically modeled as an optimal control one, and optimal solutions are synthesized. The predictive optimal real-time algorithm is confronted to the adaptive method. Both control strategies are simulated on different driving cycles. The simulation results show the interest of predictive approaches for hybrid electrical vehicles energy management. 1 INTRODUCTION Hybridization has been introduced in car industry es- sentially to reduce fuel consumption, which leads to a reduction of CO 2 and pollutants emissions. The con- cept of hybridization is to add another (clean) energy source to the classical fossil fuel bringing another en- ergy converter. In this paper, attention is focused on Hybrid Electrical Vehicle where besides the Internal Combustion Engine (ICE), the power-train is also me- chanically connected to an electrical machine. One of the major interests of electrical hybridization is the reversible aspect of the energetic flow on the Electri- cal Machine Actuator (EMA). The electrical machine can convert the vehicle kinetic energy into electrical energy stored in the battery. In a hybrid vehicle, the combination of two energy sources creates a free en- ergetic node. Regardless of the drivers behavior, the combination ratio of the two energy sources is a new degree of freedom which must be set by real-time em- bedded control. The question that constitutes the energy manage- ment problem of this paper is : How can the fuel en- ergy, consumed by ICE, be minimized in a way to ob- tain a certain electrical energy balance over the trip? In other words, knowing the future path, the objec- tive is to use electrical on-board energy to minimize the fuel consumption over the path, and at the same time, retrieve a targeted state of charge of the battery. A problem that is also referred to as the TorqueSplit problem. Researches on this energy management topic started many years ago (Sciarretta and Guzzella, 2007). Two approaches have been adopted. First, heuristic methods for real-time use, such as rule based methods, fuzzy logic (Caux et al., 2010), stochastic strategies or other strategies such as the equivalent consumption minimization strategy (Sciarretta et al., 2004). Although this type of methods has the advan- tage of being compatible with real-time use, it does not guarantee a rigorous optimal solution. On the other hand, model-based methods using optimal con- trol theory and dynamic programming algorithms, in off-line computation (Tribioli and Onori, 2013), (Del- prat et al., 2003), can guarantee optimal solutions without being real-time compatible. Idrissi Hassani Azami, H., Caux, S., Messine, F. and Sans, M. Optimal Control for Energy Management of Connected Hybrid Electrical Vehicles. DOI: 10.5220/0006668302610268 In Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2018), pages 261-268 ISBN: 978-989-758-293-6 Copyright c  2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved 261