Hybrid deep learning model for efficient state of charge estimation of Li-ion batteries in electric vehicles

Exciting News! Our novel research on Li-ion battery technology in electric vehicles (EVs) has been accepted for publication in the renowned journal of Energy, with an impact factor of 9.0.

Level 2 Journal: Our work has been acknowledged as a Level 2 journal publication according to the Norwegian Scientific Index. This distinction further reinforces the academic value and impact of our study.

Battery Coast Strategy: This paper holds immense relevance to the Battery Coast Strategy at the University of Agder. The Battery Coast serves as a bridge, connecting interdisciplinary battery development and research streams from academia and industry across the entire battery value chain. By fostering collaboration with major battery players in southern Norway and internationally, along with establishing an application-oriented Battery Engineering education, we actively co-create battery competence.

Title: "Hybrid deep learning model for efficient state of charge estimation of Li-ion batteries in electric vehicles"

Authors: Muhammad Hamza Zafar, Majad Mansoor, Mohamad Abou Houran, Noman Mujeeb Khan, Kamran Khan, Syed Kumayl Raza Moosavi, and Filippo Sanfilippo.

Abstract: State of charge (SoC) estimation is critical for the safe and efficient operation of electric vehicles (EVs). This work proposes a hybrid multi-layer deep neural network (HMDNN)-based approach for SoC estimation in EVs. This HMDNN uses Mountain Gazelle Optimizer (MGO) as a training algorithm for the deep neural network. Our method leverages the intrinsic relationship between the SoC and the voltage/current measurements of the EV battery to accurately estimate the SoC in real time. We evaluate our approach on a large dataset of real-world EV charging data and demonstrate its effectiveness in comparison to traditional SoC estimation methods. Four diverse Li-ion battery datasets of electric vehicles are employed which are the dynamic stress test (DST), Beijing dynamic stress test (BJDST), federal urban driving schedule (FUDS), and highway driving schedule (US06) with different temperatures of 0oC,25oC,45oC. The comparison is made with Mayfly Optimization Algorithm based DNN, Particle Swarm Optimization based DNN and Back-Propagation based DNN. The evaluation indices used are normalized mean square error (NMSE), root mean square error (RMSE), mean absolute error (MAE), and relative error (RE). The proposed algorithm achieves 0.1% NMSE and 0.3% RMSE on average on all datasets, which validates the effective performance of the proposed model. The results show that the proposed neural network-based approach can achieve higher accuracy and faster convergence than existing methods. This can enable more efficient EV operation and improved battery life.

Article Link: Hybrid deep learning model for efficient state of charge estimation of Li-ion batteries in electric vehicles

Possible collaborations: Morrow Batteries