In this paper, the computation of eco-driving cycles for electric, conventional and hybrid vehicles using receding horizon and optimal control is studied. The problem is formulated as consecutive-optimization problems aiming at minimizing the vehicle energy consumption under traffic and speed constraints. The impact of the look-ahead distance and the optimization frequency on the optimal speed computation is studied to find a trade-off between the optimality and the computation time of the algorithm. For the three architectures considered, simulation results show that in urban driving conditions, a look-ahead distance of 300m to 500m leads to a sub-optimality less than 1% in the energy consumption compared to the global solution. For highway driving conditions, a look-ahead distance of 1km to 1:5km leads to a sub-optimality less than 2% compared to the global solution.

To meet future pollutant emissions standards, it is crucial to be able to estimate the cycle by cycle in-cylinder mass and the composition of the combustion chamber charge. This charge consists of residual gases from the previous cycle, fresh air and fuel. Consequently, the estimation of the fresh air mass based on total in-cylinder mass is a function of residual gas fraction. This estimation is essential to compute the fuel mass to be injected. This paper proposes an algorithm, based on physical equations, which estimates the in-cylinder total mass based on cylinder pressure. A residual gas model, which computes the burned gas fraction, is then used to determine the fresh air mass. The paper shows that the algorithm, tested on a Spark Ignited engine, is very robust to noise. To test the estimator several parameters are varied: valve timing, cylinder pressure sampling period, residual gas fraction, cylinder pressure offset and exhaust gas temperature.

International audienceTightening European standards on fuel consumption and pollutant emissions reduction lead to a sophistication of engine concepts and associated control. Since few years, downsizing (reduction of the engine displacement) appears as a major way to achieve those requirements for spark ignition engines. Efficient performance and drivability can be then achieved with a direct injection downsized engine with turbocharging and Variable Camshaft Timing (VCT). One of the major issues of the torque-oriented control is in-cylinder mass observation and control. To have an efficient torque response, the in-cylinder trapped mass, adjusted by the throttle and the waste gate, must be controlled with accuracy according to performance and drivability requirements. Depending on admission and exhaust pressures, the twin VCT will allow to control in-cylinder burned gases rate to reduce fuel consumption and pollutant emissions, and air scavenging to improve transient speed response. Another major issue is in-cylinder trapped mass and air scavenging prediction for AFR control. In this paper, we propose a model-based approach to achieve those engine control issues. The first challenge is to design accurate observers for non-measurable variables (in-cylinder burned gases and trapped air mass). The method is based on a complex high frequency 0D engine model, which has been validated on a large range of engine operating points and transient operations based on test bed results. Then, this model permits to design and learn open-loop nonlinear static observers of in-cylinder masses (based on neural network). The static and dynamic behavior of high frequency 0D engine model allows to achieve design of dynamic and closed-loop in-cylinder mass observation and prediction, multivariable and non-linear control of air path according to in-cylinder mass trajectory (trapped air mass & recirculated gases rate). Then, the complete engine control can be developed and validated on simulation and on a real time Software-In-the-Loop platform based on high frequency 0D engine model, before a complete validation and calibration on test bed. Finally, the complete torque-oriented engine control has been integrated on vehicle. From 0D engine model, a complete vehicle model has been set on real-time platform in order to validate engine control integration and design vehicle layout. The major issue is then supervision of engine control set points (torque, AFR, efficiency) according to engine states (start, idle, driver request, cut-off) and warm-up strategies

The paper proposes a generic methodology to incorporate constraints (pollutant emission, battery health, drivability) into on-line energy management strategies (EMSs) for hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs). The integration of each constraint into the EMS, made with the Pontryagin maximum principle, shows a tradeoff between the fuel consumption and the constraint introduced. As state dynamics come into play (catalyst temperature, battery cell temperature, etc.), the optimization problem becomes more complex. Simulation results are presented to highlight the contribution of this generic strategy, including constraints compared to the standard approach. These results show that it is possible to find an energy management strategy that takes into account an increasing number of constraints (drivability, pollution, aging, environment, etc.). However, taking these constraints into account increases fuel consumption (the existence of a trade-off curve). This trade-off can be sometimes difficult to find, and the tools developed in this paper should help to find an acceptable solution quickly