State-of-Health Estimation of Li-ion Batteries in Electric Vehicle Using IndRNN under Variable Load Condition

Abstract

In electric vehicles (EVs), battery management systems (BMS) carry out various functions for effective utilization of stored energy in lithium-ion batteries (LIBs). Among numerous functions performed by the BMS, estimating the state of health (SOH) is an essential and challenging task to be accomplished at regular intervals. Accurate estimation of SOH ensures battery reliability by computing remaining lifetime and forecasting its failure conditions to avoid battery risk. Accurate estimation of SOH is challenging, due to uncertain operating conditions of EVs and complex non-linear electrochemical characteristics demonstrated by LIBs. In most of the existing studies, standard charge/discharge patterns with numerous assumptions are considered to accelerate the battery ageing process. However, such patterns and assumptions fail to reflect the real world operating condition of EV batteries, which is not appropriate for BMS of EVs. In contrast, this research work proposes a unique SOH estimation approach, using an independently recurrent neural network (IndRNN) in a more realistic manner by adopting the dynamic load profile condition of EVs. This research work illustrates a deep learning-based data-driven approach to estimate SOH by analyzing their historical data collected from LIBs. The IndRNN is adapted due to its ability to capture complex non-linear characteristics of batteries by eliminating the gradient problem and allowing the neural network to learn long-term dependencies among the capacity degradations. Experimental results indicate that the IndRNN based model is able to predict a battery’s SOH accurately with root mean square error (RMSE) reduced to 1.33% and mean absolute error (MAE) reduced to 1.14%. The maximum error (MAX) produced by IndRNN throughout the testing process is 2.5943% which is well below the acceptable SOH error range of ±5% for EVs. In addition, to demonstrate effectiveness of the IndRNN attained results are compared with other well-known recurrent neural network (RNN) architectures such as long short-term memory (LSTM) and gated recurrent unit (GRU). From the comparison of results, it is clearly evident that IndRNN outperformed other RNN architectures with the highest SOH accuracy rate.

A Review on UAV Wireless Charging: Fundamentals, Applications, Charging Techniques and Standards

“…Future research is directed towards the development of protecting the wireless charging system against attacks such as Jamming attacks [165]- [168], Spoofing attacks [166], [196], Safety attacks [196], [197], Interference attacks [196], Software attacks [166], [196], [198] and Monitoring attack [196]. Furthermore, research is directed towards the development of energy-saving systems [199], handling large UAV protocol data [200], avoiding charging conflicts [169]- [174], design of novel coil structures [149], [151], [152], drone to drone (V2V) charging [175], Supercapacitor based fast charging [176], dynamic charging of UAV during long flight hours [177], [178], Efficient battery management of UAV for optimized performance [179]- [181], development of coordination algorithms [182], [183], using UAVs for 5G/6G communication [184], [185], accurate drone landing algorithms using image processing techniques [186]- [188] and studying effects of EM waves on the human body [189].…”

Comparison of machine learning techniques for SoC and SoH evaluation from impedance data of an aged lithium ion battery

“…ML techniques such as Fuzzy Logic (FL), Support Vector Machine (SVM) and Artificial Neural Networks (ANN) have extensively been applied for the estimation of the health of batteries, and a brief review can be found in [3]. In most cases, SoH is estimated by determining battery capacity and internal resistance, parameters strictly related to SoH, from input variables behaviour analysis (current, temperature, voltage, etc.)…”

“…An application of Fuzzy logic with a potential use in portable devices is reported in [19], where Electrochemical Impedance Spectroscopy (EIS) technique was used for the dataset creation. However, improper hypotheses in the Fuzzy rules [3] and reduced set of observations can lead to substantial errors. The Support Vector Machine is a regression algorithm which converts nonlinearities in a lower dimension space to a linear model developed in a higher-dimensional one [20].…”

“…Artificial Neural Networks (ANNs) are probably the most used approach, inspired by the biological functioning of the human brain, for modeling nonlinear systems. SoH estimation using an independently recurrent neural network (in RNN) was realised in [3]. Here SoH was predicted accurately with a root means square error (RMSE) of 1.33% and mean absolute error (MAE) of 1.14%.…”

Long Short-Term Memory Approach to Estimate Battery Remaining Useful Life Using Partial Data

“…The lithium-ion (Li-ion) battery is widely used in electric vehicles because of its exceptional high cell voltage, high energy density, electromotive force, high output voltage, long lifetime, high charging efficiency, low self-discharge, low voltage drop, easy maintenance, and recycle [2], [3], [4]. These advantages have contributed to wider applications of lithium-ion batteries in more areas such as vehicles, household equipment, communications, aerospace, and other fields [5].…”