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Designing a hybrid powertrain remains a complex task. It is an intricate system involving numerous variables that are spread over different levels: architecture, component technologies, sizing, and control. There is currently a lack of frameworks or tools that help in exploring the entire design space and in finding the global optimal solution throughout these levels. This article proposes a systematic methodology that tries to answer a part of this need. Starting from a set of chosen components, the methodology automatically generates all the possible graphs of architectures using constraint-programming techniques. A tailored representation is developed to picture the graphs. They are then transformed into other types of representation (tables describing the connections and the powertrain modes). Based on these representations, the architectures are automatically filtered and the most promising ones are selected. They are automatically assessed and optimized using a specifically developed general hybrid model that calculates the performance and fuel consumption of all the generated architectures. This model is inserted inside a bi-level optimization process: a Genetic Algorithm is used on the sizing and components level, while the Dynamic Programming is used on the control level. A case study is performed and the capability of the methodology is proven.

Among Series-Parallel Hybrid Electric Vehicle (SPHEV) powertrains, the Power-Split architecture with a planetary gear has an exemplary energetic efficiency in mixed driving conditions. Nevertheless, a simple SPHEV architecture can be realized without a planetary gear. It consists of 2 Electric Machines (EM) mounted on the engine shaft and separated by a clutch. With no power-split operation, this architecture allows the vehicle to operate in pure electric, or series hybrid, or parallel hybrid mode. It was proven to be less efficient than a reference Power-Split SPHEV: the Toyota Hybrid System (THS). The aim of this paper is to investigate the potential of efficiency improvement of the simple SPHEV powertrain by topology modification: the addition of gears for the components or a gearbox with few number of ratios. Two new variants of SPHEVs are proposed. The versions of SPHEVs and the reference THS are optimized by a bi-level optimization technique using Genetic Algorithm and Dynamic Programming. Compared to the simple SPHEV, results show an efficiency worsening in one variant and an efficiency improvement in another variant with a fuel consumption comparable to the one of THS. A global sensitivity study is then performed on the worsened variant. The sensitivities of the added gears are determined and an elimination of some is suggested. A new variant with fewer gears is therefore proposed and optimized. The efficiency is improved but remains less than the one of THS.

AAC 2019, 9th IFAC Symposium on Advances in Automotive Control, ORLEANS, FRANCE, 23-/06/2019 - 27/06/2019; As an alternative to power-split hybrid architectures, a simple series-parallel architecture named SPHEV 2 can be realized. In this paper, the sizing process of this architecture is briefly presented and a deeper analysis is made. Mathematical sensitivity analysis studies are conducted on the sizing parameters of the architecture in order to bring more understanding to the optimization results. Local and global sensitivities are performed to understand the influence of the sizing variables on the fuel consumption. An analysis of the sensitivities is also made.

VPPC, BELFORT, FRANCE, 11-/12/2017 - 14/12/2017; A simple version of a Series-Parallel Hybrid Electric Vehicle (SPHEV) powertrain was proposed and studied in previous work. It consists of 2 Electric Machines (EM) mounted on the engine shaft and separated by a clutch. It is a relatively simple architecture that allows vehicle operation in pure electric, series hybrid and parallel hybrid modes. However, this architecture was proven to be less efficient than a reference Power-Split SPHEV architecture: the Toyota Hybrid System (THS). As a continuation, this paper investigates the potential of improvement of the simple SPHEV by the addition of gears and a gearbox.Two new variants of SPHEVs are therefore proposed. The three versions of SPHEVs and the reference THS are sized on a global optimal manner and compared. Results show that one variant of the simple SPHEV can have comparable fuel consumption to the THS.

International audience; This research work aims to study the impact of the heating system on the fuel consumption of a hybrid electric vehicle (HEV). The thermal engine is less used in an HEV than in a thermal vehicle, thus the cabin heating is partly ensured by electrical resistances. However, because the battery is partly charged by the thermal engine, this electrical heating has an impact on the fuel consumption. In the present work, a multi-domain model is proposed to analyze the impact of the heating system on the fuel consumption of a HEV. The models of the different physical subsystems are organized and unified by energetic macroscopic representation (EMR). Experimental validations, with an accuracy of 95%, are provided for each subsystem model. The validated simulation models are used to study the impact of the heating system for a specific driving cycle and climatic condition. For a simple energy management strategy (EMS), there is an over-consumption of 19% that is due to the heating system. When a more efficient EMS is used, the over-consumption is reduced to 12%. This study shows the interest in developing advanced energy management strategies that couple the traction and the heating functions of the vehicle.

Energy management strategy (EMS) is the key for efficient hybrid vehicles. Two EMS kinds can be generally used: optimal-based algorithms to reach optimum and rule-based algorithms for its easy implementation. The paper objective is to propose a hybrid optimal EMS using optimal-based and rule-based approaches to use benefits of each approach. This hybrid optimal approach enables to reduce the computation time keeping the optimal behavior. This mix is realized thanks to a multi-level structuration of the EMS. This hybrid optimal EMS is a transient step to structure the EMS design. This hybrid optimal EMS, applied on a complex hybrid vehicle enables to divide the computation time by 16 keeping the optimal behavior.