State-of-Charge Prediction Model for Ni-Cd Batteries Considering Temperature and Noise

Abstract

The accurate prediction of the state of charge (SOC) of Ni-Cd batteries is critical for developing battery management systems for high-speed trains. To address the challenges of the large floating charge voltage of Ni-Cd batteries and the vulnerability of a battery’s SOC to environmental factors such as temperature, this paper proposes an adaptive adjustment mechanism-based particle swarm optimization (APSO) generalized regression neural network (GRNN) model. The proposed model introduces the concept of the particle aggregation degree to quantify the convergence of the particle swarm optimization (PSO) algorithm. Furthermore, the speed weight of the particle swarm is adaptively adjusted using a comprehensive loss function to optimize the parameters of the GRNN model. To validate the proposed method, simulation experiments are conducted under test conditions using Ni-Cd batteries, and the prediction accuracies of various algorithms are compared. The experimental results demonstrate that the APSO-GRNN model significantly reduces the model’s prediction error. In addition, under the influence of different temperatures and noises, this method demonstrates strong robustness and high practical application value by accurately predicting the SOC, even with limited data samples.

Health Factor Experimental Testing and SOH Estimation for Nickel Cadmium Batteries

“…Nickel cadmium batteries have become the most commonly used batteries in EMU auxiliary power supply system due to their advantages such as long lifespan, low price, resistance to overcharging, high and low temperature resistance, and the ability to charge at high rates [1,2]. Estimating and predicting the state of health (SOH) for nickel cadmium batteries is a core issue in battery management systems [3].…”

“…The existing health factors and health estimation methods for lithium batteries are difficult to apply to nickel cadmium batteries, and they are also unable to cope with the health state estimation under high-speed train conditions. In addition, Cheng et al [2] provided an assessment of the life status of nickel cadmium batteries in high-speed trains, while DAI et al [3] provided a prediction of the life of nickel cadmium batteries, providing reference for battery life evaluation and optimization of maintenance strategies. However, it is difficult to achieve quantitative and accurate SOH estimation.…”