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Energy management strategies for an electric vehicle (EV) with multiple power sources have been widely described in literature. The investigated energy sources are batteries, ultracapacitors, fuel cells, flywheels and solar panels. The management strategy decides how to combine two or more sources in an optimal way. However, the behavior of these sources and also the behavior of the electric drives depend on their temperature. Moreover, the temperature can have extreme values in automotive applications and affect the energy management task. In this paper, to investigate the temperature effect on battery/ultracapacitor powered EV, temperature dependent models are presented for these storage components, as well as for the drive train components itself: power electronics and motor. The average motor iron loss and ultracapacitor loss tend to decrease with increasing temperature, while the average motor copper loss and power electronics loss tend to increase with increasing temperature. These two opposing trends cause the total loss of the drive train to have a rather small variation with temperature for the considered EV and in the considered temperature range. By consequence, the energy management strategy of the EV does not have to depend on the temperature in order to obtain maximal efficiency. (C) 2017 Elsevier Ltd. All rights reserved.

Cold extrusion is a process commonly used to manufacture drive train components in the automotive industry. Large plastic strains can be applied during this operation (up to 1.5) and greatly changes the mechanical properties of the resulting material. This study focuses on the impact of cold extrusion process parameters on the multiaxial fatigue behaviour of steel components. A specific set of forward rod extrusion tools was developed to get original fatigue specimen able to characterise the effect of the manufacturing process on the fatigue behaviour. The specimens were extruded from two different initial diameters, giving two different reductions in cross-section of 18% and 75% respectively. To understand the influence of cold extrusion, the following analyses have been undertaken for each condition and on the initial material: monotonic tensile properties, microstructure, EBSD, residual stresses and hardness. Simulation of the forming process and microstructural observations show that the plastic strain is homogeneous in the specimen section. For both reduction factors, the forming process has a positive effect on the components properties: induced residual stresses in compression and improved hardness and roughness (Ra decreasing). Tension, plane bending and torsion fatigue tests show that the fatigue strength is about 30% higher for the batch with 75% reduced cross-section. All investigations show that strain hardening is the principal material parameter responsible for the increase in fatigue strength. A multiaxial fatigue criterion taking into account the effects of the forward rod extrusion process was also developed. This work has been performed within the ANR (National Research Agency) DEFISURF project, in a partnership including several industrial (Ascometal, Cetim, PSA, Transvalor, Atelier des Janves, Gévelot) and academic (INSA Lyon MATEIS, ENSMP-CEMEF, Arts et Métiers ParisTech LAMPA) institutions.

Hybrid vehicles require a supervisory algorithm, often referred to as energy management strategy, which governs the drivetrain components. In general the energy management strategy objective is to minimize the fuel consumption subject to constraints on the components, vehicle performance and driver comfort. Typically, we have to deal with two difficulties in the design of an energy management strategy. Firstly, the nonlinear behavior of the components results in a nonconvex cost function, complicating the use of optimization methods. Different approaches to deal with the nonconvexity are discussed. Secondly, the future power and velocity trajectories are unknown. Prediction of the future trajectories, based upon either past or predicted vehicle velocity and road grade trajectories, could help in obtaining a solution close to optimal. The benefit of prediction, compared to a heuristic and an optimal control strategy that uses only actual vehicle data, is shown with an example of a hybrid truck at a highway trajectory in a hilly environment. Results indicate that prediction has benefits only when the slopes have sufficient grade and length, such that the battery state-of-charge boundaries are reached. © 2010 IFAC.

Hybrid vehicles require a supervisory algorithm, often referred to as energy management strategy, which governs the drivetrain components. In general the energy management strategy objective is to minimize the fuel consumption subject to constraints on the components, vehicle performance and driver comfort. Typically, we have to deal with two difficulties in the design of an energy management strategy. Firstly, the nonlinear behavior of the components results in a nonconvex cost function, complicating the use of optimization methods. Different approaches to deal with the nonconvexity are discussed. Secondly, the future power and velocity trajectories are unknown. Prediction of the future trajectories, based upon either past or predicted vehicle velocity and road grade trajectories, could help in obtaining a solution close to optimal. The benefit of prediction, compared to a heuristic and an optimal control strategy that uses only actual vehicle data, is shown with an example of a hybrid truck at a highway trajectory in a hilly environment. Results indicate that prediction has benefits only when the slopes have sufficient grade and length, such that the battery state-of-charge boundaries are reached. © 2010 IFAC.