• Energy Research

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.

Hybrid vehicles use two energy sources for their propelling. Usually an internal combustion engine (ICE) is used with one or more electric machine(s) (EM). The problem is then to split the driver power demand between the ICE and the EM in order to minimize a criterion, usually the fuel consumption. A global optimization algorithm based on optimal control theory is recalled. The obtained results are optimal but can only be obtained in simulation. For real time control purpose, this optimization algorithm is applied on a receding horizon. The main problem is then to choose the variables to be predicted on this horizon. By analyzing the optimization algorithm, it is shown that the prediction of the future driving conditions (vehicle speed and driver torque demand) is not necessary. Therefore, under some assumptions, a real time control is possible.

The present paper is dedicated to the investigation of a predictive Equivalent Consumption Minimization Strategy. The objective is to determine the torque split between the internal combustion engine and the electric machine of a hybrid vehicle. The energy management is formulated as a receding optimization problem. To avoid a complex prediction of the vehicle speed and acceleration over time, the slow dynamic of their distribution is exploited. A rational tuning of the algorithm parameters is proposed as well as some improved implementations. The number of individual operations (additions, multiplications, interpolations, etc) required per seconds is discussed. Finally, the energy management algorithm energy consumption are assessed over different driving cycles, including one with a \boldsymbol 15406 ${km}$ length obtained using GPS measurements. A comparison with an adaptive Equivalent Consumption Minimization Strategy is provided. The predictive Equivalent Consumption Minimization Strategy allows controlling the state of charge close to a (possibly time varying) set point while providing low fuel consumption.

When designing hybrid vehicles, the energy management is formulated as an optimal control problem. The Pontryagin's minimum principle represents a powerful methodology capable of solving the energy management offline. Moreover, the Pontryagin's minimum principle has been proved useful in the derivation of online energy management algorithms, such as the equivalent consumption minimization strategy. Nevertheless, difficulties on the application of the Pontryagin's minimum principle arise when state constraints are included in the definition of the problem. A possible solution is to combine the Pontryagin's minimum principle with a penalty function approach. This is done by adding functions to the Hamiltonian, which increase the value of the Hamiltonian whenever the optimal trajectory violates its constraints. However, the addition of penalty functions to the Hamiltonian makes it harder to compute its minimum. This work proposes an effective penalty approach through an implicit Hamiltonian minimization. The proposed method is applied to solve the energy management for a hybrid electric vehicle modeled as a mixed input-state constrained optimal control problem with two states: the battery temperature and state-of-energy. It is demonstrated to be up to 46 times faster than the dynamic programming method while taking benefits of state-of-the-art boundary value problem solvers and avoiding any issue related to state quantization.

The French national MEGEVH project deals with energy management of hybrid electric vehicles (HEV). The objective is to provide general methodologies to model HEV and optimize their energy flows. The project is subdivided in modeling and optimization sub-projects. Graphical modeling has been chosen to highlight energetic properties of the studied vehicles. Two actual vehicles are taking as reference for the developments. Theoretical methods will be first developed independently of the kind of HEV. Then these methods will be tested on the reference vehicles. The MEGEHV objectives and organization are firstly detailed. In a second section, Energetic Macroscopic Representation of a parallel HEV is described as an example of use of graphical modeling for energy management.

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.

Hybrid vehicle energymanagement is often studied in simulation as an optimal control problem. Under strict convexity assumptions, a solution can be developed using Pontryagin’s minimum principle. In practice, however, many engineers do not formally check these assumptions resulting in the possible occurrence of so-called unexplained “numerical issues.” This paper intends to explain and solve these issues. Due to the binary controlled-state variable considered (e.g., switching on/off an internal combustion engine) and the use of a lookup table with linear interpolation (e.g., engine fuel consumption map), the corresponding Hamiltonian function can have multiple minima. Optimal control is not unique. Moreover, it is defined as being singular. Consequently, an infinite number of optimal state trajectories can be obtained. In this paper, a control law is proposed to easily construct a few of them.

Hybrid electric vehicles generally use an internal combustion engine and one or several electric drives. Optimization-based energy management strategies computes a power split between both the engine and the machine to minimize the fuel consumption. Nevertheless, the driving comfort is not taken into account. Due to its high torque bandwidth, the electric machine can be used to improve driving comfort. The objective of this paper is to propose the use the electric machine to actively damp the encountered side-shafts torque oscillations. The driveshaft torsional behavior is described and the corresponding comfort issues are highlighted. Simulations results have been done for the nominal case and to validate the controller behavior with respect to some un-modeled parameter variations.