Data Preparation and Training Methodology for Modeling Lithium-Ion Batteries Using a Long Short-Term Memory Neural Network for Mild-Hybrid Vehicle Applications

Abstract

Voltage models of lithium-ion batteries (LIB) are used to estimate their future voltages, based on the assumption of a specific current profile, in order to ensure that the LIB remains in a safe operation mode. Data of measurable physical features—current, voltage and temperature—are processed using both over- and undersampling methods, in order to obtain evenly distributed and, therefore, appropriate data to train the model. The trained recurrent neural network (RNN) consists of two long short-term memory (LSTM) layers and one dense layer. Validation measurements over a wide power and temperature range are carried out on a test bench, resulting in a mean absolute error (MAE) of 0.43 V and a mean squared error (MSE) of 0.40 V2. The raw data and modeling process can be carried out without any prior knowledge of LIBs or the tested battery. Due to the challenges involved in modeling the state-of-charge (SOC), measurements are used directly to model the behavior without taking the SOC estimation as an input feature or calculating it in an intermediate step.

Spatiotemporal Joint Cleaning of Distribution Network Measurement Data Based on Correntropy Criterion with Variable Center Unscented Kalman Filter

“…The CC-VC-UKF algorithm obtains the indeterminate-point iterative equation according to the minimized cross-correlation entropy, as well as the Kalman gain through indeterminate-point iteration. Complex physical connections exist between various nodes and lines in the power grid, with a strong correlation [23]. Therefore, the measurement data collected by the…”

“…Complex physical connections exist between various nodes and lines in the power grid, with a strong correlation [23]. Therefore, the measurement data collected by the measurement device also have a time and space correlation, as shown in Figure 3.…”

“…The Minkowski distance is one of the most widely used algorithms in similarity measurement. It requires that the two sequences to be compared have the same length and that the points of the two time series correspond one-to-one [23] to rapidly calculate the Minkowski distance between the two sequences.…”

Energy Management Control and Optimization for Hybrid Electric Vehicles

“…Apart from providing a simultaneous estimation of battery open-circuit voltage, more rapid and less fluctuating battery capacity estimation were the main advan-tages of this new proposed monitoring structure. In [10], a completely different approach to model Lithium-Ion batteries was presented that did not require any prior knowledge of these batteries or theoretical analysis. It was based on training recurrent neural networks, composed of two short-term memory layers and one dense layer with processed data of measurable physical features-current, voltage, and temperature.…”