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Considering a trip completed by a driver, in which the effects of infrastructure (roundabouts, intersections, traffic conditions) divide the trip in segments. An important problem in eco-driving is the energy minimization by optimizing one’s driving style inside each segment while maintaining the trip’s time duration. To this aim, this paper inherits principles of optimal off-line control problems with space and time boundary conditions. The novelty concerns the introduction of a multi-objective approach applied to the optimal control. The proposed optimization problem aims to find the best combination of segment’s time durations without influencing the global final time and minimizing the trip’s corresponding energy consumption. In order to solve this problem, a graph based approach is proposed and the optimal solution is obtained through the application of the Dijkstra’s shortest path algorithm.

With the steep increase of heavy-duty transportation, more fuel efficient technologies and services have become of great importance due to their environmental impact (in terms of CO 2 emissions) and economical impact (costs for the fleet managers). This work addresses the problem of finding the less expensive itinerary among several ones provided by a Geographic Information System (GIS). The problem focuses on the prediction of a speed profile as realistic and as representative of the route as possible and physically feasible for a truck. The novelty of the proposed tool lies on the precise understanding of the road as well as on the study of the vehicle's parameters impact on the fuel consumption. This work is of great interest for a truck fleet manager wanting to quantify the fuel cost of the trip's external conditions while varying for instance the truck's cargo.

This paper considers an energy management problem for an electric vehicle compliant with online requirements for ‘eco-driving’ application. Eco-driving techniques are discussed and formulated as an optimal control problem that consists of the minimization of the vehicle consumption over a time and distance horizon. Then, a closed-form solution of the optimal trajectories is presented. Numerical and experimental results assess the validity of the proposed eco-driving system.

To meet the growing need for mobility of people and goods while massively reducing CO2 emissions, the electrification of vehicles is an essential solution. The variety of vehicles and their use results in innovative solutions for adapted architecture. This is especially true for light commercial vehicles where the objective is to promote full electric use in urban conditions (zero emission vehicle) while maintaining significant range autonomy on road. The project VelRoue, a partnership between Renault, Michelin and IFP Energies nouvelles, aims to develop a dedicated dual-mode vehicle using a conventional thermal powertrain on the front axle and in-wheel motors on the rear one each powertrain to its use and makes it possible to achieve a low level of homologation CO2 emissions. In addition to features that meet the specific use of a commercial vehicle, in this paper we will particularly demonstrate the benefit of such an architecture to optimize the regenerative braking while ensuring a safe dynamic behaviour.

Energy management of hybrid propulsion systems is considered, presenting new issues that extend the energy management role beyond the standard torque splitting to maximize system efficiency. The new issues include additional optimization criteria, constraints and relevant dynamics to deal with. New optimization criteria in addition the sole fuel consumption minimization include engine-out pollutant emissions and battery aging. State constraints are modified to account for plug-in hybrid vehicles. Moreover, specific supervisory control problems are recognized to need additional state variables. The latter comprise: engine and catalyst temperature to deal with engine warm-up effects on fuel consumption and after-catalyst emissions; thermal dynamics of heat recovery systems (Rankine or Thermo-Electric Generators, TEGs); and battery temperature, which influences battery performance and aging. It is shown that all these control problems can be treated in an unified fashion by extending the well-known ECMS (Equivalent Consumption Minimization Strategy), which is an implementation of Pontryagin Minimum Principle (PMP) as formulated by optimal control theory. Extended definitions of the Hamiltonian function and Lagrange multipliers are introduced. Optimization runs performed off line are reported. Results show the benefits of the proposed unified approach and enlighten some first online implementation issues.

In this paper, an approach to evaluate the energy efficiency of an electrical vehicle during a short trip is addressed. This approach provides metrics that can be used to evaluate the potential of improvement achievable via intelligent eco-driving techniques and the performance actually achieved by any of them. It can be the basis for an evaluation of a fleet of vehicles and of a whole city for which an energy efficiency map could be derived, depending for instance of traffic management strategy. Experimental data are provided to illustrate the approach for a fleet of electric vehicles in an urban environment. The methods proposed are intended to be the basis of a driver assistance system oriented to optimal eco-driving.

This paper considers an energy management problem for an electric vehicle compliant with online requirements for ‘eco-driving' application. Eco-driving techniques are discussed and formulated as an optimal control problem that consists of the minimization of the vehicle consumption over a time and distance horizon. Then, a closed form solution of the optimal trajectories is presented. Numerical and experimental results are provided.

This paper focuses on online control energy management strategy for a parallel hybrid architecture. The strategy is developed from the optimal control theory, using Pontryagin's Minimum Principle (PMP). The resulting Equivalent Consumption Minimization Strategy (ECMS) is implemented in real time and tested in simulation and in an experimental environment (HyHiL test bench). In particular, the adaptation of one control parameter, playing the role of a Lagrange multiplier, is analyzed in terms of stability and effectiveness.

Systematic optimization of modern powertrains, and hybrids in particular, requires the representation of the system by means of Backward Quasistatic Models (BQM). In contrast, the models used in realistic powertrain simulators are often of the Forward Dynamic Model (FDM) type. The paper presents a methodology to derive BQM’s of modern powertrain components, as parametric, steady-state limits of their FDM counterparts. The parametric nature of this procedure implies that changing the system modeled does not imply relaunching a simulation campaign, but only adjusting the corresponding parameters in the BQM. The approach is illustrated with examples concerning turbocharged engines, electric motors, and electrochemical batteries, and the influence of a change in parameters on the supervisory control of an hybrid vehicle is then studied offline, in co-simulation and on an HiL test bench adapted to hybrid vehicles (HyHiL).

Current engine development projects have to face crucial challenges: raw pollutant emission reduction while maintaining or even reducing the engine fuel consumption. To solve this issue, several technologies have been studied and some can offer part of the solution. Among these technologies, turbochargers provide one of the best trade off. Different turbocharging technologies can be used as well as different air path layouts, but one particularly promising technology relies in the Two-Stage series turbocharging. Coupled to turbocharging devices, and despite the recent development in NO after-treatment systems, EGR remains a cost-effective solution to respect current and future emissions standards. Thanks to the widespread use of Diesel Particulate Filters, High Pressure EGR is no longer the only efficient solution to provide exhaust gas to the cylinders, in so far as one of the main drawbacks of Low Pressure EGR – compressor and air intake cooler reliability – is reduced. In this context, it is difficult to assess intuitively the impact of the different possible air system configurations on the engine performance, and the consequences on the associated control strategies. It is therefore necessary to use advanced tools, such as simulation in general and engine system simulation in particular. In this paper, we present the way engine system simulation can help in the understanding of technological issues as well as control issues. The approach is illustrated by the study of the impact of Low Pressure EGR in the specific case of a Two-Stage Turbocharged Diesel Engine. Indeed, the impact on the air path layout and on the engine performances are presented, and comparisons between simulation results of the same engine with a High Pressure or a Low Pressure EGR circuit are analyzed. In parallel, some specificities of the control strategy of a Two-Stage Turbocharged Diesel engine are discussed. The impact of a LP EGR circuit on the control laws is analyzed and a solution to adapt the strategies is finally proposed.

The paper describes a chain of tools aimed at the development and validation of energy management strategies (EMS) for hybrid powertrains. These tools comprise an offline optimizer based on Pontryagin minimum principle (PMP) and the online equivalent consumption minimization strategy (ECMS), both implemented in a dynamic simulation platform and as a real-time controller in a semi-physical testing equipment. The results presented are aimed at illustrating the continuity of the various approaches by comparing the offline-generated energy management laws with their online counterparts, both in terms of trajectories over time and in terms of global results (fuel consumption, state-of-charge deviations).

To meet the growing need for mobility of people and goods while massively reducing CO 2 emissions, the electrification of vehicle becomes essential. One solution is the use of in-wheel motors on the rear wheels of a conventional vehicle with a thermal powertrain on front axle. It provides new opportunities to control the torque to the wheel with a fast response time. One knows that electric motors on hybrid vehicles enable energy recovery during braking, but it has to be well coordinated with overall system and especially hydraulic brake system in order to achieve safety behavior and vehicle grip in overall conditions. Furthermore, the knowledge of motor torque able to prospect new solutions to estimate and control the longitudinal friction between the road and the tires and operate efficient slip control while optimizing regenerative braking. It could then be used as the main actuator for traction control and ABS systems in both acceleration and deceleration.

This paper focuses on hybrid energy management for a Diesel hybrid electric vehicle (HEV). The paper presents an adaptation of the equivalent consumption minimization strategy (ECMS) dedicated to the Diesel HEV control issues. The purpose of this paper is to introduce an additional degree of freedom into the ECMS that allows to modify the optimization tradeoffs from the pure fuel economy case to the pure NOx limitation case. The influence of this weighting parameter is evaluated for several engine configurations. The main contribution of this paper is to provide tools and methods to deal with the Diesel HEV control task. This paper applies a methodology based on SiL and HiL simulations in order to understand the system performance according to the powertrain configurations and also to tune the proposed energy management strategy. The simulation results are confirmed by experiments performed on hybrid-hardware in the loop (Hy-HiL) test bench. These results demonstrate the ability of the proposed strategy to reduce the NOx emissions while maintaining a reasonable fuel consumption. This result is mostly due to the ability of the modified ECMS to operate the Diesel engine within its LTC region where the NOx emissions are very low.

The present paper deals with the activities and the results achieved under a cooperative research project between IFP, D2T, LMS, G2Elab and Renault focused on Hardware-in-the-Loop (HIL) applications to hybrid power-trains conception and assessment. The main goal of this study is the evaluation of hybrid propulsion concepts and the benefits of different degrees of hybridization in a flexible architecture, by using a chain of simulation platforms: from the co-simulation to the high-dynamic engine-in-the-loop test bed, through a virtual version of the last one. This paper focuses on the implementation issues of an energy management strategy (EMS) for a parallel hybrid architecture. The EMS is developed from the optimal control theory, using Pontryagin's Minimum Principle (PMP). The resulting Equivalent Consumption Minimization Strategy (ECMS) is implemented in real time and tested in an experimental environment (HyHiL test bench). In particular, a possible integration of the three way catalytic converter constraint in the optimization is presented.

In recent years, shortening of the total development cycle for a completed vehicle and reduction of on-board vehicle calibration are increasing in demand in the research and development process in the automotive industry. To meet this demand, the solution is to provide well-suited testbenches. For that, dynamic estimation of the torque produced by the engine is required. The purpose of the paper is to provide a dynamic estimation of the engine torque. Convergence of the observer is proven and experimental results are reported.