Towards long lifetime battery: AI-based manufacturing and management
Technologies that accelerate the delivery of reliable battery-based energy storage will not
only contribute to decarbonization such as transportation electrification, smart grid, but also …
only contribute to decarbonization such as transportation electrification, smart grid, but also …
Integration of energy storage system and renewable energy sources based on artificial intelligence: An overview
AN Abdalla, MS Nazir, H Tao, S Cao, R Ji… - Journal of Energy …, 2021 - Elsevier
Energy storage technology plays a role in improving new energy consumption capacities,
ensuring the stable and economic operation of power systems, and promoting the …
ensuring the stable and economic operation of power systems, and promoting the …
[HTML][HTML] Online capacity estimation of lithium-ion batteries with deep long short-term memory networks
There is an increasing demand for modern diagnostic systems for batteries under real-world
operation, specifically for the estimation of their state of health, for example, via their …
operation, specifically for the estimation of their state of health, for example, via their …
Cooperative energy management and eco-driving of plug-in hybrid electric vehicle via multi-agent reinforcement learning
The advanced cruise control system has expanded the energy-saving potential of the hybrid
electric vehicle (HEV). Despite this, most energy-saving researches for HEV either only …
electric vehicle (HEV). Despite this, most energy-saving researches for HEV either only …
[HTML][HTML] A comprehensive review on energy storage in hybrid electric vehicle
The sharp inclination in the emissions from conventional vehicles contribute to a significant
increase in environmental issues, besides the energy crises and low conversion efficiency …
increase in environmental issues, besides the energy crises and low conversion efficiency …
[HTML][HTML] One-shot battery degradation trajectory prediction with deep learning
The degradation of batteries is complex and dependent on several internal mechanisms.
Variations arising from manufacturing uncertainties and real-world operating conditions …
Variations arising from manufacturing uncertainties and real-world operating conditions …
Deep reinforcement learning based energy management strategy of fuel cell hybrid railway vehicles considering fuel cell aging
K Deng, Y Liu, D Hai, H Peng, L Löwenstein… - Energy conversion and …, 2022 - Elsevier
In the rail transportation industry, growing energy and environmental awareness requires
the use of alternatives to combustion engines. These include hybrid electrically driven …
the use of alternatives to combustion engines. These include hybrid electrically driven …
A novel energy management strategy for hybrid electric bus with fuel cell health and battery thermal-and health-constrained awareness
In the field of future transportation, hydrogen fuel cell hybrid electric vehicles (FCHEVs) are
regarded as the most potential renewable energy vehicles, but improper use of the Lithium …
regarded as the most potential renewable energy vehicles, but improper use of the Lithium …
[HTML][HTML] Data-driven systematic parameter identification of an electrochemical model for lithium-ion batteries with artificial intelligence
Electrochemical models are more and more widely applied in battery diagnostics,
prognostics and fast charging control, considering their high fidelity, high extrapolability and …
prognostics and fast charging control, considering their high fidelity, high extrapolability and …
Battery-involved energy management for hybrid electric bus based on expert-assistance deep deterministic policy gradient algorithm
Energy management is an enabling technique to guarantee the reliability and economy of
hybrid electric systems. This paper proposes a novel machine learning-based energy …
hybrid electric systems. This paper proposes a novel machine learning-based energy …