Abstract
In this study, a vehicle localization technique was employed to determine the required quantities in the identification of battery models by considering the behavior of multiple batteries instead of data from a single battery. In previous studies, a plant (e.g., a battery, motor, super-capacitor, or fuel cell) was identified based on a single piece of data. However, such an approach is disadvantageous in that it neglects the effect of process and measurement noise and assumes that the parameters obtained using data from a single plant are identical for all plants of the same type. First, deterministic parameter estimation (DPE), particle swarm optimization (PSO), and teaching-learning-based optimization (TLBO) were initially applied to estimate the battery model parameters using data from a single battery. Second, a fusion-based approach was used to address the process and measurement noise problems through an adaptive unscented Kalman filter algorithm. With this approach, maximum likelihood estimation was employed to fuse multiple-battery data streams to enable the DPE, PSO, and TLBO to recalculate the model parameters based on filtered and fused quantities. A comparison between the experimental results and model outputs obtained using the aforementioned methods for parameter estimation indicated that the proposed multiple-battery approach enhances the accuracy of several identification methods. In contrast, it requires a high computational effort.
Highlights
L ITHIUM batteries are widely used in various fields
Thereafter, a MATLAB package [49] was used to read the sensors connected to the Arduino board in real time and store the data in the MATLAB environment
The terminal voltage and current recorded from a single battery (i.e., B1) as a result of 60-Ω continuous load discharge were inputted into the deterministic parameter estimation (DPE), particle swarm optimization (PSO), and teaching-learning-based optimization (TLBO) to identify α7– α21
Summary
L ITHIUM batteries are widely used in various fields. their price is anticipated to decrease over time as the market for this type of batteries further grows [1]. An example is described in [21], in which an adaptive observer was employed to estimate the electrochemical model parameters of a lithium-ion battery This method requires large battery packs with a highly varying current profile, its main advantage is that it can reduce the computational time by splitting a complex model into four subsystems. The MLE fusion technique discussed in [29] was employed to average the estimated terminal voltages according to their associated measurement noise covariances. An adaptive UKF was applied to estimate the battery model output, capacity, and dynamic process and measurement noise covariances for the data of each battery. Each capacity state estimated by the adaptive UKF was fused with related quantities from other battery data based on their associated process noise covariances using the MLE technique.
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