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Model-based adaptive joint estimation of the state of charge and capacity for lithium–ion batteries in their entire lifespan

Chen, Z; Xiao, J; Shu, X; Shen, S; Shen, J; Liu, Y (MDPI, 2020-01-01)

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). In this paper, a co-estimation scheme of ...

Cooperative control strategy for plug-in hybrid electric vehicles based on a hierarchical framework with fast calculation

Zhang, Y; Guo, C; Li, G; Liu, Y; Chen, Z (Elsevier, 2020-04-01)

Developing optimal control strategies with capability of real-time implementation for plug-in hybrid electric vehicles (PHEVs) has drawn explosive attention. In this study, a novel hierarchical control framework is proposed ...

State of Health Estimation for Lithium-Ion Batteries Based on Healthy Features and Long Short-Term Memory

Wu, Y; Xue, Q; Shen, J; Lei, Z; Chen, Z; Liu, Y (IEEE, 2020-01-01)

Precise estimation of state of health (SOH) are of great importance for proper operation of lithium-ion batteries equipped in electric vehicles. For real applications, it is however difficult to estimate battery SOH due ...

Online energy management strategy of fuel cell hybrid electric vehicles based on rule learning

Liu, Y; Liu, J; Qin, D; Li, G; Chen, Z; Zhang, Y (Elsevier, 2020-07-01)

In this paper, a rule learning based energy management strategy is proposed to achieve preferable energy consumption economy for fuel cell hybrid electric vehicles. Firstly, the optimal control sequence of fuel cell power ...

Remaining Useful Life Prediction for Lithium-ion Batteries Based on Capacity Estimation and Box-Cox Transformation

Xue, Q; Shen, S; Li, G; Zhang, Y; Chen, Z; Liu, Y (IEEE, 2020-01-01)

IEEE Remaining useful life (RUL) prediction of lithium-ion batteries plays an important role in intelligent battery management systems (BMSs). The current RUL prediction methods are mainly developed based on offline training, ...

A novel optimal power management strategy for plug-in hybrid electric vehicle with improved adaptability to traffic conditions

Zhang, Y; Wei, C; Liu, Y; Chen, Z; Hou, Z; Xu, N (Elsevier, 2021-03-31)

Adaptability to various driving conditions (TCs) is one of the essential indicators to assess the optimality of power management strategies (PMSs) of plug-in hybrid electric vehicles (PHEVs). In this study, a novel optimal ...

Data-driven based eco-driving control for plug-in hybrid electric vehicles

Li, J; Liu, Y; Zhang, Y; Lei, Z; Chen, Z; Li, G (2021-06-30)

With the development of connected and automated vehicles, eco-driving control is reckoned to generate unprecedented potential on energy-saving in electrified powertrain. In this paper, a data-driven based eco-driving control ...

Driving Behavior Oriented Torque Demand Regulation for Electric Vehicles with Single Pedal Driving

Zhang, Y; Huang, Y; Chen, H; Na, X; Chen, Z; Liu, Y (Elsevier, 2021-04-01)

Driving behaviors, induced by psychological activities and environment stimulation, impose the dominant impact on vehicle driving performance. To exhaustively improve the performance of electric vehicles (EVs), information ...

A Flexible State of Health Prediction Scheme for Lithium-Ion Battery Packs with Long Short-Term Memory Network and Transfer Learning

Shu, X; Shen, J; Li, G; Zhang, Y; Chen, Z; Liu, Y (2021-01-01)

Dynamic Lane-Changing Trajectory Planning for Autonomous Vehicles Based on Discrete Global Trajectory

Liu, Y; Zhou, B; Wang, X; Li, L; Cheng, S; Chen, Z; Li, G; Zhang, L (IEEE, 2021-01-01)

Automatic lane-changing is a complex and critical task for autonomous vehicle control. Existing researches on autonomous vehicle technology mainly focus on avoiding obstacles; however, few studies have accounted for dynamic ...