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Found 9 papers, 3 papers with code
Remaining Energy Prediction for Lithium-Ion Batteries: A Machine Learning Approach
In this paper, we present the first study on predicting the remaining energy of a battery cell undergoing discharge over wide current ranges from low to high C-rates.
Lane-Change in Dense Traffic with Model Predictive Control and Neural Networks
This paper presents an online smooth-path lane-change control framework.
Parking of Connected Automated Vehicles: Vehicle Control, Parking Assignment, and Multi-agent Simulation
This paper introduces a novel approach to optimize the parking efficiency for fleets of Connected and Automated Vehicles (CAVs).
Online energy management system for a fuel cell/battery hybrid system with multiple fuel cell stacks
The online energy management system (EMS) is essential for these hybrid systems, tasked with controlling the energy flow and ensuring optimal system performance, encompassing fuel efficiency and mitigating fuel cell and battery degradation.
HumanLight: Incentivizing Ridesharing via Human-centric Deep Reinforcement Learning in Traffic Signal Control
Apart from adopting FRAP, a state-of-the-art (SOTA) base model, HumanLight introduces the concept of active vehicles, loosely defined as vehicles in proximity to the intersection within the action interval window.
Robo-Chargers: Optimal Operation and Planning of a Robotic Charging System to Alleviate Overstay
Then, with operation model nested, we develop a planning model to guide economical investment on both types of chargers so that the total cost of ownership is minimized.
Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries
This paper proposes two new frameworks to integrate physics-based models with machine learning to achieve high-precision modeling for LiBs.
Integrating Electrochemical Modeling with Machine Learning for Lithium-Ion Batteries
Mathematical modeling of lithium-ion batteries (LiBs) is a central challenge in advanced battery management.
Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network
This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of other drivers.
