Authors:
Elkhatib Kamal
and
Lounis Adouane
Affiliation:
Institut Pascal/IMobS3, France
Keyword(s):
Artificial Intelligence, Battery Management System, Fuzzy Observer, Hybrid Electric Vehicles, Power Management Strategy, Sensor Faults, Takagi-sugeno Fuzzy Model.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Control and Supervision Systems
;
Fuzzy Control
;
Fuzzy Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Neural Networks Based Control Systems
;
Optimization Algorithms
;
Robotics and Automation
;
Soft Computing
Abstract:
This paper deals with a robust energy management strategy, including a battery fault detection and compensation
for a hydraulic-electric hybrid vehicle. The overall control and management strategy aims to minimize
total energy consumption while ensuring a better battery life. Many power management strategies do not consider
battery faults which could accelerate battery aging, decreasing thus its life and could cause also thermal
runaway, which may cause fire and battery explosions. Therefore, battery fault tolerant control to guarantee the
battery performance is also proposed in this paper. The proposed strategy consists of fuzzy supervisory fault
management at the highest level (the second). This level is responsible to detect and compensate the battery
faults, generating optimal mode and healthy state of charge set point for first level to prevent overcharge or/and
over-discharge. In the first level, an energy management strategy is developed based on neural fuzzy strategy
to manage power distribution between electric motor and engine. Then, there are robust fuzzy controllers
to regulate the set points of each vehicle subsystems to reach the best operational performance. The Truck-
Maker/MATLAB simulation results confirm that the proposed architecture can satisfy power requirement for
any unknown driving cycles and compensate battery faults effect.
(More)