Nonlinear adaptive sliding mode control of a powertrain supplying Fuel Cell hybrid vehicle

Abstract

This paper presents an adaptive sliding mode based switching scheme for controlling DC-DC hybrid powertrain for propulsion of a Fuel Cell / Supercapacitor hybrid vehicle. After modeling the powertrain, a new approach to determine a nonlinear sliding surface ensuring stability of the DC/DC Boost converter is discussed. This was achieved without introducing the equivalent control aspect after transforming the instantaneous model of the Boost in a suitable form. The presented technique is also applied for trajectories tracking in the entire powertrain, which includes a dc/dc Boost converter associated to Fuel Cell stack and another Bidirectionnel dc/dc converter associated to the supercapacitor bank, which are both working in parallel to provide electricity propelling the vehicle. The control scheme is tested with driving cycle example through simulation.

Adaptive Sliding Mode Control Method for Onboard Supercapacitors System

“…3 Design of an Adaptive, Robust, RBF Neural Network Sliding Mode Controller Due to the state discontinuity caused by frequent on-off events, the converter has nonlinear characteristics and is easily affected by disturbances, so the control effect is not ideal. A sliding mode variable structure control algorithm with independent parameter inputs and disturbances is used to design the controller [21][22][23][24], which is then switched by the control quantities in order to make the system slide along the sliding mode surface. To overcome the chattering phenomenon on both sides of the sliding mode surface, a neural network is added to approximate the nonlinear relationship between the sliding mode surface and the control quantity, so the system has good robustness and anti-interference characteristics.…”

Control Method for PEMFC Using Improved Deep Deterministic Policy Gradient Algorithm

“…As a new type of chemical power source, the fuel cell generation process is not a direct combustion of fuel compared to thermal power generation, the power generation efficiency is not limited by the Carnot cycle and the emission of harmful substances is extremely low (Yang et al, 2020;Yang et al, 2018). Its energy conversion rate is as high as 80 %, and its actual efficiency is double that of an ordinary internal combustion engine (Bougrine et al, 2013) The fuel cell is therefore a new power source with high efficiency and clean features, combining new technologies in energy, chemicals, materials and automatic control (Yang et al, 2019a;Yang et al, 2021c).…”

Distributed Imitation-Orientated Deep Reinforcement Learning Method for Optimal PEMFC Output Voltage Control

“…There are a number of existing PEMFC output voltage control methods based on control of DC-DC converters, including the PID control algorithm (Swain and Jena, 2015), fractional order PID algorithm (Yang et al, 2019a;Yang et al, 2019b;Yang et al, 2020), sliding mode control algorithm (Bougrine et al, 2013;Jiao and Cui, 2013), model predictive control algorithm (Bemporad et al, 2002;Ferrari-Trecate et al, 2002), robust control method (Olalla et al, 2010), and optimal control algorithm (Jaen et al, 2006;Olalla et al, 2009;Montagner et al, 2011;Moreira et al, 2011) methods, and so on. Among them, the PID algorithms are traditional control algorithms whose advantages include simple structure and fast calculation speed.…”