• Energy Research

Energy storage systems are a key point in the design and development of electric and hybrid vehicles. In order to reduce the battery size and its current stress, a hybrid storage system, where a battery is coupled with an electrical double-layer capacitor (EDLC) is considered in this paper. The energy management of such a configuration is not obvious and the optimal operation concerning the energy consumption and battery RMS current has to be identified. Most of the past work on the optimal energy management of HEVs only considered one additional power source. In this paper, the control of a hybrid vehicle with a hybrid storage system (HSS), where two additional power sources are used, is presented. Applying the Pontryagin's minimum principle, an optimal energy management strategy is found and compared to a rule-based parameterized control strategy. Simulation results are shown and discussed. Applied on a small compact car, optimal and ruled-based methods show that gains of fuel consumption and/or a battery RMS current higher than 15% may be obtained. The paper also proves that a well tuned rule-based algorithm presents rather good performances when compared to the optimal strategy and remains relevant for different driving cycles. This rule-based algorithm may easily be implemented in a vehicle prototype or in an HIL test bench.

Abstract A good way to reduce the fuel consumption of a commercial river ship can be the hybridization of the system. Sizing the system related to its actual use and energy management is then a key-point. In this paper a global sizing method based on Genetic Algorithm and dynamic programming is proposed. It allows the sizing of a complex series-parallel architecture on uses cycles which are developed using measurements on real ship. The study shows potential reduction of fuel consumption around 10%, and proposes some leads to reduce the pollutant emissions.

Current transportation systems contributed to one quarter to the Greenhouse Gases (GhG) emissions and the mobility demand increases continuously. The transportation infrastructure design has to be optimized to mitigate these emissions. The methodology “Sloop” (acronym for Slope Optimization) has recently been set up to optimize the longitudinal road profile with respect to a Global Warming Potential (GWP) criterion calculated for both the construction and operational phases while incorporating accurate vehicle models. This paper proposes a customized optimization strategy that significantly improves the Sloop methodology. From an initial longitudinal profile generated by road designers, our algorithm identifies the optimized profile in terms of GWP. This optimization step is complex due to the large number of degrees of freedom, along with various constraints to obtaining a feasible solution and the computational cost of the operational phase assessment. The most stringent constraint entails connecting the profile with the existing road (i.e., a constraint on the final profile altitude). We have developed a specific algorithm based on the Sequential Quadratic Programming (SQP) method; this algorithm takes into account the quasi-quadratic nature of the constraint on the final altitude. In a real case study, our algorithm outputs a profile whose GWP assessment saves 4% compared to the GWP assessment of the initial profile. The benefit of our specific protocol for treating the final altitude constraint is demonstrated in this example. By means of this new efficient and open-box algorithm, profile evolution at each iteration is analyzed to determine the most sensitive degrees of freedom, and the sensitivity of the optimization with respect to the main construction parameters and traffic assumptions are conducted. The results are consistent and stable.

Automotive engineers and researchers have proposed different topologies for series-parallel hybrid electric vehicles (SP-HEVs). The Toyota Hybrid System (THS) is the best known SP-HEV-based vehicle, but alternative solutions, such as electric variable transmission (EVT), have been also proposed. An efficient comparison between these different solutions is a key point to estimate the added value of each topology. This paper presents the application of optimal control to two series-parallel hybrid architectures for efficiency assessment purposes. The dynamic programming method is applied to the THS and to a virtual hybrid vehicle with an EVT. The way to take into account the supplementary degree of freedom provided by the decoupling of the wheels and the engine in both topologies is presented. The optimal fuel consumption is then compared on different driving cycles and brings out an overconsumption of the EVT topology. Then, a parametric study shows that inserting an appropriate gear ratio on the internal-combustion-engine (ICE) shaft can improve the EVT efficiency that becomes close to the THS efficiency.

Abstract In the context of direct-current electrified railway systems, power supply quality issues are a major concern for the railway network operators. Indeed, after signaling systems and track equipment, voltage issues may be the most challenging factor for the capacity of a direct-current railway line. These issues and the high cost of new substations motivate the implementation of wayside energy storage systems to support the system. Since an appropriate sizing methodology is a key step towards an energy storage system implementation in a railway line, in this paper a sizing methodology is presented in detail. As hypothesis, the sizing methodology is defined such that the storage system is able to support the railway line under projected conditions of the rolling stock traffic, along with other technical criteria relevant to the railway operator. A real-time simulation oriented, direct-current railway network modeling approach is proposed and exploited in this sizing methodology. Using this modeling method, the optimal energy storage sizing formulation is described. The objective function to minimize is the trade-off between energy storage capacity and charging power. The proposed sizing method is applied to a real railway line with known power supply quality issues. This case study is introduced by analyzing real voltage measurements on two key sites of the line over several days. Then, the sizing methodology is applied and the results are discussed for the study case, defining the minimal technical requirements to install. The obtained minimal technical requirements for the storage system are considered by the operator to take a cost-effective decision in contrast with the reinforcement of the line with new substations.

Battery lifetime is an important parameter in the life cycle assessment (LCA) of a plug-in hybrid-electric vehicle (PHEV). This paper aims to study the impact of various parameters on the battery aging of a PHEV. For this purpose, model-based use cases are generated, the outputs of which are the daily driven distances for a period of one year, recharge scenarios, and battery temperature. A combined aging model (calendar and cycling aging) is used to calculate the capacity lost by the battery at the end of one year of use. The thermal model of the battery is using an electro-thermal coupling equation, for which the ambient temperature is modeled using daily minimum and maximum temperature data varying throughout the year for different cities. Finally, a sensitivity analysis is carried out using the conditioned variance method to identify the most important input parameters which largely affect the output of this study. The results of this study show that battery size, annual mileage, external temperature, and charging behavior are the most important parameters to be considered in the aging study of the battery of a PHEV personal car.