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.

A sustainable circular economy involves designing and promoting new products with the least environmental impact through increasing efficiency. The emergence of autonomous vehicles (AVs) has been a revolution in the automobile industry and a breakthrough opportunity to create more sustainable transportation in the future. Autonomous vehicles are supposed to provide a safe, easy-to-use and environmentally friendly means of transport. To this end, improving AVs’ safety and energy efficiency by using advanced control and optimization algorithms has become an active research topic to deliver on new commitments: carbon reduction and responsible innovation. The focus of this study is to improve the energy consumption of an AV in a vehicle-following process while safe driving is satisfied. We propose a cascade control system in which an autonomous cruise controller (ACC) is integrated with an energy management system (EMS) to reduce energy consumption. An adaptive model predictive control (AMPC) is proposed as the ACC to control the acceleration of the ego vehicle (the following vehicle) in a vehicle-following scenario, such that it can safely follow the lead vehicle in the same lane on a highway. The proposed ACC appropriately switches between speed and distance control systems to follow the lead vehicle safely and precisely. The computed acceleration is then used in the EMS component to find the optimal engine torque that minimizes the fuel consumption of the ego vehicle. EMS is designed based on two methods: type 1 fuzzy logic system (T1FLS) and interval type 2 fuzzy logic system (IT2FLS). Results show that the combination of AMPC and IT2FLS significantly reduces fuel consumption while the ego vehicle follows the lead vehicle safely and with a minimum spacing error. The proposed controller facilitates smarter energy use in AVs and supports safer transportation.

This paper proposes an intelligent energy management system (EMS) based on Multi-Armed Bandit (MAB) algorithm to enhance vehicle powertrain efficiency and reduce emissions in Conventional Autonomous Vehicles (CAVs) in a spark-ignition engine. The presented EMS includes Support Vector Machine (SVM) and a multi-objective MAB. The Multi-objective MAB algorithm aims to minimize fuel consumption and emissions. The MAB requires adaptive and online classification of data based on environment and vehicle specification. The SVM algorithm works inside the MAB algorithm to create adaptive online classification. The algorithm produces optimal torque for fuel consumption by choosing the best throttle angle based on online classification. The MAB algorithm chooses a suitable throttle angle to maintain a stoichiometric air/fuel ratio for the least emissions. The proposed controller adjusts engine torque to decrease fuel usage and CO and NOx emissions. The simulation results show that the proposed EMS can decrease vehicle fuel usage to 6.41 l/100km, 11.7% less than the vehicle without the controller. The designed EMS also decreases CO and NOx engine emissions by 4.5% and 4.4%, respectively.