A Novel Deep Learning-Based State-of-Charge Estimation for Renewable Energy Management System in Hybrid Electric Vehicles

Abstract

In recent years, alternative engine technologies are necessary to resolve the problems related to conventional vehicles. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are effective solutions to decarbonize the transportation sector. It also becomes important to shift from traditional houses to smart houses and from classical vehicles to EVs or HEVs. It is needed to combine renewable energy sources (RESs) such as solar photovoltaics, wind energy systems, and various forms of bio-energies. Among various HEV technologies, an effective battery management system (BMS) still remains a crucial issue that is majorly used for indicating the battery state of charge (SOC). Since over-charging and over-discharging result in inevitable impairment to the batteries, accurate SOC estimation desires to be presented by the BMS. Although several SOC estimation techniques exist to regulate the SOC of the battery cell, it is needed to improvise the SOC estimation performance on HEVs. In this view, this paper focuses on the design of a novel deep learning (DL) with SOC estimation model for secure renewable energy management (DLSOC-REM) technique for HEVs. The presented model employs a hybrid convolution neural network and long short-term memory (HCNN-LSTM) model for the accurate estimation of SOC. In order to improve the SOC estimation outcomes of the HCNN-LSTM model, the barnacles mating optimizer (BMO) is applied for the hyperpower tuning process. The utilization of the HCNN-LSTM model makes the modeling process easier and offers a precise depiction of the input–output relationship of the battery model. The design of BMO based HCNN-LSTM model for SOC estimation shows the novelty of the work. An extensive experimental analysis highlighted the supremacy of the proposed model over other existing methods in terms of different aspects.

Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems

“…In order to further assure the enhanced performance of the HBFOA-SAE model, a comparative examination with the deep learning based SOC (DLSOC) and optimal extreme learning machine (ELM) models takes place under distinct measures as shown in Tab. 4 [20,21]. After examining the results and discussion, it is confirmed that the HBFOA-SAE model has shown effective SOC estimation outcomes.…”

Advancing State of Charge Management in Electric Vehicles With Machine Learning: A Technological Review

“…Some current methods, designed to handle complex battery behaviors, may introduce overcomplication when applied to simpler battery systems. For EVs with straightforward battery designs, the added complexity may not necessarily translate into proportional benefits, leading to inefficiencies in terms of computational resources and implementation costs [70][71][72]. In simple battery systems, the added complexity resulted in an 11.17% increase in computational resources without significant improvement in accuracy, highlighting the inefficiency of applying complex models [51,54].…”

“…The capacity degradation over time due to chemical processes and charge-discharge cycles poses a considerable challenge for precise SOC estimation [71][72][73].…”

Control and estimation techniques applied to smart microgrids: A review

“…Ref. [160] presents an intelligent estimation technique based-deep learning to estimate the SOC of BESS in conjunction with VRE coordination with hybrid EVs. The estimation of BESS is essential for microgrids, and an OMT can provide several approaches to optimise the BESS charging and discharging process.…”