Intelligent Battery Power System Design and Simulation

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Prof. Cheng Siong Chin
1. Faculty of Science, Agriculture, and Engineering, Newcastle University in Singapore, Singapore 599493.
2. School of Automotive Engineering; The State Key Laboratory of Mechanical Transmissions; Chongqing Automotive Collaborative Innovation Center, Chongqing University, Chongqing, China, 400044.
Email:cheng.chin@ncl.ac.uk

Prof. Cai Zhi Zhang
School of Automotive Engineering; The State Key Laboratory of Mechanical Transmissions; Chongqing Automotive Collaborative Innovation Center, Chongqing University, Chongqing, China, 400044.
Email:czzhang@cqu.edu.cn

Dr. Zuchang Gao
School of Engineering, Clean Energy Research Centre, Temasek Polytechnic, 21 Tampines Avenue 1, Singapore 529757.
Email:zuchang@tp.edu.sg

Manuscript Topics
Lithium-ion batteries have received attention from both the scientific community and the industry in electric vehicles, underwater robotics vehicle and airplane. Compared with other commonly used batteries like lead acid, nickel cadmium (NiCd) and nickel metal hydride (NiMH), they offer advantages such as high energy density and power density. The lithium-ion batteries have to operate within operating specifications to prevent rapid performance degradation. For example at a different operating ambient temperature under large time-varying discharging and charge current, the state-of-charge (SOC) performance will be affected. Battery aging is another important factor to be considered in battery power system design and simulation. The electrochemical model is an accurate battery model for estimating the SOC. However, electrochemical models can be quite complicated to use. As a result, the black-box models using artificial intelligence are proposed in the literature. However, suffers from the high computational time due to training requirements. Effective system design, simulation, and implementation are therefore necessary when using the artificial intelligence techniques to address such challenges.
This special issue welcomes the original research articles, having a contribution in theoretical and experimental analysis aimed at further understanding of intelligent techniques, on the design, modelling, and implementation of SOC estimation, the state of health (SOH) and active balancing of lithium-ion batteries in an uncertain environment. Review articles related to these application areas on electric and hybrid electric vehicles, ship, storage of renewable energy and uninterruptible power supply (UPS) systems are also invited.

• Thermal modelling
• Electro-thermal modelling
• Electrochemical-thermal modelling
• Predictive modelling
• Adaptive modelling
• Battery management system
• Fault identification and detection
• Communication system
• Swarm intelligence and evolutionary algorithms
• Machine learning methods
• Artificial neural networks

Paper Submission
All manuscripts will be peer-reviewed before their acceptance for publication.
The deadline for manuscript submission is June 30, 2019.

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