State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles under Dynamic Load Conditions

Abstract

Among numerous functions performed by the battery management system (BMS), online estimation of the state of health (SOH) is an essential and challenging task to be accomplished periodically. In electric vehicle (EV) applications, accurate SOH estimation minimizes failure risk and improves reliability by predicting battery health conditions. The challenge of accurate estimation of SOH is based on the uncertain dynamic operating condition of the EVs and the complex nonlinear electrochemical characteristics exhibited by the lithium-ion battery. This paper presents an artificial neural network (ANN) classifier experimentally validated for the SOH estimation of lithium-ion batteries. The ANN-based classifier model is trained experimentally at room temperature under dynamic variable load conditions. Based on SOH characterization, the training is done using features such as the relative values of voltage, state of charge (SOC), state of energy (SOE) across a buffer, and the instantaneous states of SOC and SOE. At implementation, due to the slow dynamics of SOH, the algorithm is triggered on a large-scale periodicity to extract these features into buffers. The features are then applied as input to the trained model for SOH estimation. The classifier is validated experimentally under dynamic varying load, constant load, and step load conditions. The model accuracies for validation data are 96.2%, 96.6%, and 93.8% for the respective load conditions. It is further demonstrated that the model can be applied on multiple cell types of similar specifications with an accuracy of about 96.7%. The performance of the model analyzed with the confusion matrices is consistent with the requirements of the automotive industry. The classifier was tested on a Texas F28379D microcontroller unit (MCU) board. The result shows that an average real-time execution speed of 8.34 µs is possible with a negligible memory occupation.

A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

“…However, this method has obvious drawbacks. Firstly, it is difficult to obtain the initial value of the battery SOE accurately [56]. In the power integration method, an inaccurate initial value is utilized to predict the SOE, which easily causes the accumulation of errors [45].…”

Investigation of Degradation of Solar Photovoltaics: A Review of Aging Factors, Impacts, and Future Directions toward Sustainable Energy Management

“…For 15 g of dust deposition, agricultural dust such as mud, rice husk, compost, etc., can result in a maximum power loss of 51.82% [96]. On the other hand, industrial dust such as gypsum and coal can decrease a panel's efficiency by 64% and 42%, respectively [99]. Therefore, it is evident that this will decrease the PV panel's transmittance and result in partial shadowing, both of which will shorten the panel's lifespan [96].…”

A Future Perspective on Waste Management of Lithium-Ion Batteries for Electric Vehicles in Lao PDR: Current Status and Challenges

“…The machine learning method is the prevalent method of SoH estimation of LIBs in EVs and is accurate due to the diverse procedure for SoH estimation. The main process of machine learning methods includes data collection, feature extraction, model training, and SoH estimation [ 84 , 85 ]. Generally, when LIBs’ power capacity is less than 80%, they are not suitable for EV usage, and if the electric capacity is less than about 40%, they are no longer suitable for any commercial use.…”