Mathematical Modeling of Li-Ion Battery Using Genetic Algorithm Approach for V2G Applications

By Kannan Thirugnanam, Joy T. P. Ezhil Reena, Mukesh Singh, Praveen Kumar. IEEE transactions on energy conversion, 2014

128 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b21532c97d591f8bf51673993859cec35847a547

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Lucian Mihet-Popa
2. Chau Yuen
3. Kannan Thirugnanam

In Electrical and Electronic Engineering

1. Lucian Mihet-Popa
2. Nian Liu
3. Chau Yuen
4. Kannan Thirugnanam

Citations in the corpus

• Energy Management for Renewable Microgrid in Reducing Diesel Generators Usage With Multiple Types of Battery (2018, methodology): “In this context, a proper battery energy management system (BEMS) is necessary, such that it can minimize the use of DGs and yet confirm both reliable supply of electricity to the end users and extend batteries’ lifetime [9], [10].”
• Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications (2023, methodology): “Various types of DDMs, such as Artificial Neural Network (ANN) [43], Adaptive Neuro-Fuzzy Inference Systems (ANFIS) [44], Deep Neural Network (DNN) [45], and Support Vector Machine (SVM) [46], [47], [48], have been employed for battery modeling, as shown in Fig.”