A feedforward deep neural network for predicting the state-of-charge of lithium-ion battery in electric vehicles

Author:

Adedeji Bukola Peter,Kabir GolamORCID

Publisher

Elsevier BV

Subject

Modeling and Simulation,Applied Mathematics,Analysis,General Decision Sciences

Reference53 articles.

1. Parametric predictions for pure electric vehicles;Adedeji;World Electr. Veh. J.,2021

2. A novel lumped thermal characteristic modeling strategy for the online adaptive temperature and parameter co-estimation of vehicle lithium-ion batteries;Shi;J. Energy Storage,2022

3. Technological developments in batteries;Hu;IEEE Power Energy Mag.,2017

4. A novel cuckoo search particle filtering strategy for the remaining useful life prediction of the lithium-ion batteries in hybrid electric vehicle;Pu;Int. J. Energy Res.,2022

5. Battery Solution for a Long and a Safe Battery Life, Battery Solution for a Long and a Safe Battery Life, Technical Article, 2022, www.powerelectronicsnews.com/bms-solutions-for-a-long-and-safe-batterylife/#:~:text=To%20measure%20the%20temperature%20in%20and%20around%20the,to%20protect%20the%20battery%20from%20overheating%20during%20work. Accessed on 04 July 2022.

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