This paper proposes an intelligent energy management strategy for a hydraulic-electric hybrid vehicle in order to minimize its total energy consumption. It proposes first to model the vehicle total energy consumption and investigates the minimization of an expended energy function, formulated as the sum of electrical energy provided by the on-board batteries and consumed fuel. More precisely, it is proposed in this paper an intelligent controller which shows its capabilities of increasing the overall vehicle energy efficiency and therefore minimizing total energy consumption. The proposed strategy consists of an advanced supervisory controller at the highest level (third) which corresponds to a fuzzy system deciding the most appropriate operating mode of the system. In the second level, an intelligent optimal control strategy is developed based on neuro-fuzzy logic. Then, in the first level, there are local fuzzy controllers to regulate vehicle subsystems set points to reach the best operational performance. The advantage of the proposed strategy could be summarized as follows: (i) it can be implemented online; (ii) reduces total energy consumption compared with several traditional methods. The proposed strategy validations are performed using a mix of automotive TruckMaker and MATLAB/Simulink developed software on several (standard or not) driving cycles.

Reduction of fuel consumption is an indispensable part of automotive industry in recent years. This induces several developments of hybrid vehicles with different structures. This paper deals with reliable and robust energy management strategy for a hybrid hydraulic-electric intelligent vehicle. The main objective of this paper is the development of a suboptimal control strategy based on fuzzy logic and neural network for minimizing total energy consumption while ensuring a better battery life. For this purpose, fuzzy supervisory fault management, that can detect and compensate the battery faults, regulates all of the possible vehicle's operation modes. Then, control strategy based on fuzzy logic controller (FLC) is developed. The FLC membership function (MF) parameters are tuned by employing neural network to manage power distribution between electric motor and internal combustion engine (ICE). Control strategy is switched between optimized FLCs to enhance the suboptimal power split between the different energy sources and manage the ICE to work always in the vicinity of its optimal condition. Finally, a robust fuzzy tuning controllers are investigated to give a good torque set point tracking. Simulation results, while using TruckMaker/MATLAB software, confirm that the proposed approach leads to suboptimal energy consumption of the vehicle for any unknown driving cycles and compensate battery faults effects.

This paper proposes an overall Multi-Controller Architecture (MCA) for safe and flexible navigation of autonomous navigation, under uncertainties in highway use-cases. In addition to the details given about the main modules (and their interactions) composing the proposed MCA, an important focus of the paper is made on the definition of a robust Two-Sequential Level Decision Network (TSLDN), which uses both: Extended Time-To-Collision (ETTC) metric and a new definition of a specific Predicted Inter-Distance Profile (PIDP, between vehicles during lane changes maneuvers) in order to estimate the maneuvers risks. The TSLDN is utilized for: the driving situation assessment, decision-making and for safety retrospection over the current maneuver risk. It allows us to have the best decision to achieve the vehicle navigation task while maximizing its safety. Several simulation results show the good performance of the overall proposed control architecture, mainly in terms of efficiency to handle probabilistic decision-making even for very risky scenarios.