Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed.

In this paper, we develop a game theoretic model predictive controller (GTMPC) with aggressiveness estimation to deal with the mandatory lane change (MLC) problem in presence of several surrounding vehicles. GTMPC is responsible for driving a subject vehicle (SV) to a desired longitudinal position and executing lane-changing at the optimal moment. Specifically, GTMPC constantly establishes and solves Stackelberg games corresponding to multiple game candidate vehicles (GCV) within the game scope when SV is able to interact with GCVs by signaling a lane change intention through turn signal. GTMPC first selects one target vehicle (TV) within multiple GCVs based on the Stackelberg equilitrium, followed by estimating TV's aggressiveness based on the interaction between SV and TV, then completes the maneuver through MPC. GTMPC performance is compared with level-k game theoretic controller. Human-in-the-Loop (HIL) results showed that GTMPC is capable to safely complete the MLC by properly assessing the aggressiveness of surrounding vehicles, driven either by IDM or human drivers.

The electromyography (EMG) signal has great potential to determine the hand gestures automatically before the actual move begins. However, parameters of the sliding window along with the EMG signal, such as window size and overlapping size, as well as the number of votes in post-processing, such as majority voting, can significantly influence the gesture recognition accuracy. These phenomena have been investigated only in a few studies on a small number of subjects. The aim of this study is two-fold. First, to determine the influence of different window and overlapping sizes on the machine-learning performance using a large database consists of forty healthy subjects. Second, to develop a novel multi-window scheme to accumulate a large number of votes compared to the conventional single-window majority voting to improve gesture recognition accuracy. A large publicly available EMG dataset was used in this study. The window and overlapping sizes were varied between 50ms and 500ms, and between 0% and 80%, respectively. Six machine-learning algorithms, including k-Nearest Neighbor, Linear Discriminant Analysis, Logistic Regression, Naïve Bayes, Support Vector Machine, and Random Forest were used to classify six different hand gestures. Results show that the overall classification accuracy can be substantially improved by increasing the window size, overlapping size, and the number of votes in the majority voting strategy (p<0.05). The maximum accuracy was achieved using the Random Forest algorithm. The two-way repeated measure analysis of variance shows that the proposed multi-window scheme substantially improved the overall accuracy of the machine-learning algorithms compared to the conventional majority voting. The proposed method can be instrumental for efficient control of prosthetic or exoskeleton devices.

Accurately predicting changes in speed has a significant impact on the quality of energy management in hybrid vehicles. Many methods for predicting speed have been proposed in the literature, but few fully consider vehicle dynamics to predict speed changes. To this end, a new method is introduced to predict vehicle speed and to perform energy management for hybrid vehicles in situations where lateral dynamics plays a significant role. Based on the tire-road friction coefficient and the GPS signal, the maximum cornering speed of the vehicle, in which each tire force does not saturate, is evaluated. Then the principle of using less friction braking and using more regenerative braking, the vehicle speed prediction controller is designed. In the end, an optimal control method with a new Equivalent Factor (EF) adaptive algorithm is designed to distribute the torque of the engine and the motor, as well as the shift schedule of the gearbox. A driver-in-the-loop experiment is used to prove that the vehicle installed with the proposed speed prediction controller has an average 29.1% increase in energy efficiency compared to vehicle that do not have speed prediction controller. And, the EF adaptive algorithm keeps the battery SoC at a reasonable interval.