Abstract
The development of an accurate and robust state-of-charge (SOC) estimation is crucial for the battery lifetime, efficiency, charge control, and safe driving of electric vehicles (EV). This paper proposes an enhanced data-driven method based on a time-delay neural network (TDNN) algorithm for state of charge (SOC) estimation in lithium-ion batteries. Nevertheless, SOC accuracy is subject to the suitable value of the hyperparameters selection of the TDNN algorithm. Hence, the TDNN algorithm is optimized by the improved firefly algorithm (iFA) to determine the optimal number of input time delay (UTD) and hidden neurons (HNs). This work investigates the performance of lithium nickel manganese cobalt oxide (LiNiMnCoO2) and lithium nickel cobalt aluminum oxide (LiNiCoAlO2) toward SOC estimation under two experimental test conditions: the static discharge test (SDT) and hybrid pulse power characterization (HPPC) test. Also, the accuracy of the proposed method is evaluated under different EV drive cycles and temperature settings. The results show that iFA-based TDNN achieves precise SOC estimation results with a root mean square error (RMSE) below 1%. Besides, the effectiveness and robustness of the proposed approach are validated against uncertainties including noise impacts and aging influences.
Highlights
Environmental issues such as global warming, climate change, and carbon emissions drive the necessity to deploy battery storage technologies [1]
The fitness function performance of improved firefly algorithm (iFA) is compared with FA and particle swarm optimization (PSO)
It is observed that root mean square error (RMSE) is reported below 1% in the proposed algorithm under different electric vehicles (EV) drive cycles, while that for open-circuit voltage (OCV), the unscented particle filter (UPF), recursive least square (RLS), and proportional integral observer (PIO) is above 1%
Summary
Environmental issues such as global warming, climate change, and carbon emissions drive the necessity to deploy battery storage technologies [1]. Lithium-ion batteries are extensively employed in the automotive industry due to their attractive characteristics such as low self-discharge, long life cycle, high voltage, and high energy density [2]. The lithium-ion battery has some issues such as performance degradation with aging cycles, temperature rise, accurate charge estimation, over-charging, and over-discharging [3]. Further investigation is required on the lithium-ion battery charge estimation under a safe temperature region in electric vehicle (EV) applications. EV has a battery management system (BMS) that executes operations such as state of charge (SOC) monitoring, battery health estimation, remaining life prediction, temperature management, battery equalization, and fault diagnosis [4,5]. SOC is a crucial parameter of BMS which defines the Electronics 2020, 9, 1546; doi:10.3390/electronics9091546 www.mdpi.com/journal/electronics
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