Xianke Lin

Orcid: 0000-0001-5695-248X

Affiliations:
  • University of Ontario Institute of Technology (Ontario Tech University), Department of Automotive, Mechanical and Manufacturing Engineering, Oshawa, Canada
  • University of Michigan, Ann Arbor, MI, USA (PhD 2014)


According to our database1, Xianke Lin authored at least 29 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Multi-Agent Deep Reinforcement Learning-Based Multi-Objective Cooperative Control Strategy for Hybrid Electric Vehicles.
IEEE Trans. Veh. Technol., August, 2024

Traffic Information-Based Hierarchical Control Strategies for Eco-Driving of Plug-In Hybrid Electric Vehicles.
IEEE Trans. Veh. Technol., March, 2024

Path Planning and Tracking Control for Parking via Soft Actor-Critic Under Non-Ideal Scenarios.
IEEE CAA J. Autom. Sinica, January, 2024

2023
Uncertainty-Aware Decision-Making for Autonomous Driving at Uncontrolled Intersections.
IEEE Trans. Intell. Transp. Syst., September, 2023

Battery States Monitoring for Electric Vehicles Based on Transferred Multi-Task Learning.
IEEE Trans. Veh. Technol., August, 2023

Dynamic Traffic Prediction-Based Energy Management of Connected Plug-In Hybrid Electric Vehicles with Long Short-Term State of Charge Planning.
IEEE Trans. Veh. Technol., May, 2023

Efficient Stereo Depth Estimation for Pseudo-LiDAR: A Self-Supervised Approach Based on Multi-Input ResNet Encoder.
Sensors, February, 2023

A review of high-definition map creation methods for autonomous driving.
Eng. Appl. Artif. Intell., 2023

2022
Multi-Objective Design Optimization of a Novel Dual-Mode Power-Split Hybrid Powertrain.
IEEE Trans. Veh. Technol., 2022

Highway Decision-Making and Motion Planning for Autonomous Driving via Soft Actor-Critic.
IEEE Trans. Veh. Technol., 2022

Q-Learning-Based Supervisory Control Adaptability Investigation for Hybrid Electric Vehicles.
IEEE Trans. Intell. Transp. Syst., 2022

A Review of Second-Life Lithium-Ion Batteries for Stationary Energy Storage Applications.
Proc. IEEE, 2022

Image- and health indicator-based transfer learning hybridization for battery RUL prediction.
Eng. Appl. Artif. Intell., 2022

Vision-Based Environmental Perception for Autonomous Driving.
CoRR, 2022

Vision-based localization methods under GPS-denied conditions.
CoRR, 2022

Road Slope Prediction and Vehicle Dynamics Control for Autonomous Vehicles.
CoRR, 2022

Uncertainty-Aware Tightly-Coupled GPS Fused LIO-SLAM.
CoRR, 2022

High-Definition Map Generation Technologies For Autonomous Driving.
CoRR, 2022

2021
Improving Ride Comfort and Fuel Economy of Connected Hybrid Electric Vehicles Based on Traffic Signals and Real Road Information.
IEEE Trans. Veh. Technol., 2021

Predictive Battery Health Management With Transfer Learning and Online Model Correction.
IEEE Trans. Veh. Technol., 2021

Remaining Useful Life Prediction Using a Novel Feature-Attention-Based End-to-End Approach.
IEEE Trans. Ind. Informatics, 2021

A Neural Network Based Method for Thermal Fault Detection in Lithium-Ion Batteries.
IEEE Trans. Ind. Electron., 2021

2020
An MPC-Based Control Strategy for Electric Vehicle Battery Cooling Considering Energy Saving and Battery Lifespan.
IEEE Trans. Veh. Technol., 2020

Designing Multi-Mode Power Split Hybrid Electric Vehicles Using the Hierarchical Topological Graph Theory.
IEEE Trans. Veh. Technol., 2020

An adversarial bidirectional serial-parallel LSTM-based QTD framework for product quality prediction.
J. Intell. Manuf., 2020

A Data-Driven Power Consumption Model for Electric UAVs.
Proceedings of the 2020 American Control Conference, 2020

2019
Online Estimation of Diffusion-Induced Stress in Cathode Particles of Li-Ion Batteries.
Proceedings of the 28th IEEE International Symposium on Industrial Electronics, 2019

A Novel Deep Learning-Based Encoder-Decoder Model for Remaining Useful Life Prediction.
Proceedings of the International Joint Conference on Neural Networks, 2019

On Simplification of a Solid-State Battery Model for State Estimation.
Proceedings of the 2019 IEEE Conference on Control Technology and Applications, 2019


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