Sebastien Delprat

Control of a parallel hybrid powertrain: optimal control

Control strategies for hybrid powertrains are algorithms that choose the power split between the engine and motor of a hybrid vehicle in order to minimize the fuel consumption and/or emissions. The goal of this paper is to propose an efficient tool to evaluate minimal fuel consumption that is achievable in simulation. Several approaches have been proposed, using heuristics (Delprat et al. 1999) or dynamic programming (Brahma et al., 2000; Rimaux et al., 1999). One drawback of these approaches is the huge amount of time required to obtain solutions. The approach described here is based on optimal control theory (Lewis & Syrmos, 1995) and avoids this drawback. Moreover, it can be easily applied to a large family of parallel arrangements.

Equivalent consumption minimization strategy for parallel hybrid powertrains

Hybrid vehicles use at least two energy sources for their propelling. Usually an electric motor is used with an IC engine. Hybrid vehicles are expected to be less polluting and to have a lower fuel consumption than conventional vehicles. This paper presents an algorithm which chooses the power split between the motor and the engine in order to minimize the fuel consumption. First of all, the prototype built at the LAMIH is presented, then the equivalent consumption minimization strategy is described. First results show that a 17.5% of fuel reduction can be achieved for the CEN speed cycle.

Simulation and assessment of power control strategies for a parallel hybrid car

Abstract The aim of this paper is to propose a power control strategy for hybrid electrical vehicles. This strategy uses a fuel consumption criterion with battery charge sustaining. It is based on an instantaneous minimization of the equivalent fuel flow. Two comparisons are performed to evaluate the proposed strategy. The first one uses the loss minimization strategy of Seiler and Schröder [1], which appears to be realistic and efficient for real-time control. This strategy is also based on an instantaneous optimization and allows the battery state of charge to be taken into account. The second comparison is made with an optimal solution found for a given driving schedule. Although not realistic for real-time control, this solution is derived through a global optimization algorithm, the well-known simulated annealing method.

Fuel-Cell Hybrid Powertrain: Toward Minimization of Hydrogen Consumption

In this paper, the powertrain sizing of a fuel-cell hybrid vehicle (FCHV) is investigated. The goal is to determine the fuel-cell system (FCS) size, together with the energy storage system (ESS) size, which leads to the lowest hydrogen consumption. The power source (FCS + ESS) capabilities should also respect the vehicle driveability constraints. Batteries and supercapacitors are considered as ESSs. The power management strategy is a global optimization algorithm respecting charge sustaining of the ESS. The impacts of the driving cycle (urban, outer urban, and highway), ESS technology, and vehicle driveability constraints on hydrogen consumption are analyzed in detail.

Optimal control of a parallel powertrain: from global optimization to real time control strategy

This paper focuses on optimal control theory applied to parallel hybrid vehicles. For simulation purpose, a global optimization algorithm has been developed allowing one to obtain optimal consumption on a given driving cycle. In comparison with other global optimization methods, the proposed one has two main advantages: its quickness and the possibility to derive a real time control strategy. A first trial in that sense is done in this paper and compared with another real time strategy.

Control strategies for hybrid vehicles: optimal control

Control strategies for hybrid vehicles are algorithms that choose at each sampling time the power split between the engine and the motor. This paper presents global optimization algorithms based on the optimal control theory. The objective is to compute, in simulation, the minimum fuel consumption achievable with a given prototype on a given speed cycle. Results can be used as reference for the evaluation of real time control strategies and also for real-time control strategy synthesis.

Control strategy optimization for an hybrid parallel powertrain

Performances of hybrid vehicles in terms of fuel consumption are strongly related to their control strategy. First studies of this problem deal with instantaneous optimization algorithms (G. Pagnelli, 1999; J. Seiler and D. Schroder, 1998; K. Yamaguchi et al., 1996), designed for real time application. Second studies are based on global optimization algorithms (S. Delprat et al., 1999; S. Rimaux et al., 1999). They outperform instantaneous optimization results, but require a lot of computing time and it seems hard to derive a real time strategy from them. The paper focuses on a control strategy issue applied to the described example of a hybrid parallel single shaft architecture. A global optimization algorithm based on optimal control theory is presented. The results obtained with the optimal theory outperform the ones obtained by local and/or global strategies. A very interesting point is that this method can be easily used for real time application.

PHIL Implementation of Energy Management Optimization for a Parallel HEV on a Predefined Route

Real-time energy management of hybrid electric vehicles (HEVs) is a key point for performing effective fuel economy optimization. Offline methods have been developed for energy management optimization when the drive cycle is known. Online real-time methods can provide good results but can only ensure suboptimal management. In this paper, it is assumed that information about the route is available in advance. Using this knowledge, global optimization methods can be used in real-time control to approach optimal fuel consumption while keeping the state of charge (Soc) of the batteries at a desired level. Such a method is presented in this paper. The developed strategy is implemented in a real-time experiment using the power-hardware-in-the-loop (PHIL) principle. The measured fuel consumption and the obtained battery Soc trajectory demonstrate good performance of the proposed control.

Global Optimisation in the power management of a Fuel Cell Hybrid Vehicle (FCHV)

This paper proposes a way to investigate the benefit of hybridising a fuel cell vehicle with a second energy source such as batteries or supercapacitors packs. A global optimisation algorithm based on optimal control theory is proposed to determine an efficient power splitting between the fuel cell system (FCS) and the energy storage system (ESS). Both hybridisation and control strategy should minimise the hydrogen consumption for a given driving cycle. This method has fast computation time, and as a consequence many simulations can be performed within a short period, thus providing an interesting tool to test and compare different degree of hybridisation for various fuel cell hybrid vehicle (FCHV) parameters and driving cycles

Vehicle spacing control using robust fuzzy control with pole placement in LMI region

This work presents a robust pole placement in an LMI region for Takagi-Sugeno (TS) (Takagi T., Sugeno M., 1985. Fuzzy identification of systems and its application to modelling and control. IEEE Transactions on SMC 15 (1), 116-132.) fuzzy models. The objective is to find a set of linear matrix inequalities in order to ensure that the linear model poles of the nonlinear TS model remain in a specified region of the complex plane, even in presence of model uncertainties. As an illustration, the obtained conditions are applied to the spacing policy control of an automated electric vehicle. Simulation and real-time results are presented.