Battery State of Health Estimation with Improved Generalization Using Parallel Layer Extreme Learning Machine

Abstract

The online estimation of battery state of health (SOH) is crucial to ensure the reliability of the energy supply in electric and hybrid vehicles. An approach for enhancing the generalization of SOH estimation using a parallel layer extreme learning machine (PL-ELM) algorithm is analyzed in this paper. The deterministic and stable PL-ELM model is designed to overcome the drift problem that is associated with some conventional machine learning algorithms; hence, extending the application of a single SOH estimation model over a large set of batteries of the same type. The PL-ELM model was trained with selected features that characterize the SOH. These features are acquired as the discrete variation of indicator variables including voltage, state of charge (SOC), and energy releasable by the battery. The model training was performed with an experimental battery dataset collected at room temperature under a constant current load condition at discharge phases. Model validation was performed with a dataset of other batteries of the same type that were aged under a constant load condition. An optimum performance with low error variance was obtained from the model result. The root mean square error (RMSE) of the validated model varies from 0.064% to 0.473%, and the mean absolute error (MAE) error from 0.034% to 0.355% for the battery sets tested. On the basis of performance, the model was compared with a deterministic extreme learning machine (ELM) and an incremental capacity analysis (ICA)-based scheme from the literature. The algorithm was tested on a Texas F28379D microcontroller unit (MCU) board with an average execution speed of 93 μs in real time, and 0.9305% CPU occupation. These results suggest that the model is suitable for online applications.

A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural Networks

“…Furthermore, in Li et al [116] a parallel least squares ELM and a kernel-based ELM model combining Kmeans-FFA-KELM were proposed for regression problems, and both models were applied for estimating baseline evapotranspiration (ET 0 ), which is an important process for determining water requirements, designing an irrigation schedule, and managing agricultural water resources. In Ezemobi et al [114], the parallel layer ELM (PL-ELM) model is analyzed to estimate battery health status; the results show the model is suitable for online applications. Multiple enhanced parallel ELMs were proposed by Zehai et al [50] for remaining-useful-life prediction of integrated modular avionics.…”

On-Road Experimental Campaign for Machine Learning Based State of Health Estimation of High-Voltage Batteries in Electric Vehicles

“…Examples of data-driven methods include a support vector machine (SVM) [25][26][27][28][29], random forest (RF) [30,31], artificial neural networks (ANNs) [32][33][34][35][36][37], recurrent-neural networks (RNNs) [38], and variants such as long short-term memory (LSTM) [39][40][41][42][43][44] and a nonlinear autoregressive network with exogenous inputs (NARX) [45][46][47]. ANNs learn from historical data to predict future behavior, i.e., the learning procedure exploits a dataset representative of the battery behaviour to make predictions.…”

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

“…The model execution speed is verified as 8.34 µs in real time and with a negligible CPU occupation. In comparison, the PL-ELM performs a similar operation by regression with an execution speed of 93 µs [28]. In the real implementation, the classification algorithm is periodically triggered to extract the feature variables of the classifier in the buffer using information from an installed battery.…”

“…It is desired for an SOH estimation model to be adopted for the entirety of the cells in a battery pack. A parallel layer extreme learning machine algorithm (PL-ELM) is proposed by [28] for improved generalization of the SOH estimation model across the cells in a battery pack.…”