Prediction Of Battery Performance Research Articles

Parameter identification and sensitivity analysis of lithium-ion battery via whale optimization algorithm

The reaction mechanism of lithium-ion batteries is directly affect system safety and performance. Understanding the actual battery status through model simulation has become an important issue in battery management systems. This research proposes a non-destructive parameter identification method that uses whale optimization algorithm with unique global searching to identify the parameters of the electrochemical model and analyze the sensitivity of important parameters in the battery model. First, we establish an experimental platform and conduct four conditions, including 1C, 0.5C, 2C and one driving cycle. Moreover, 1C charge and discharge are taken as the benchmark for the parameter identification. After obtaining the key parameters of the battery, the battery performance prediction is carried out for the remaining three types. The terminal voltage of 0.5C, 2C and the road driving prediction errors are less than 15.45 mV, and the battery SOC errors are less than 1.21%. The results of parameter sensitivity studies show that the electrode-related parameters for the migration of lithium ions are the key to calculating the accuracy of the performance, and the porosity of the electrode has the greatest influence. The accurate parameter identification and the sensitivity provide a future methodology to design a proper battery model for battery management systems.

A Physically-Based Electrical Model for Lithium-Ion Cells

Prediction of battery performance is essential in assessing the technical and economic viability of battery systems. We present a novel impedance-based model of a lithium-ion cell that accounts for the dynamic response of battery cells as a nonlinear function of the state of charge (SoC). The model is composed of impedance blocks connected in series. Each block is derived from a specific electrochemical equation linked to the battery operation. The state of charge is estimated from the voltage of a nonlinear capacitance, thereby addressing the intercalation of ions into the electrode structure. The developed procedure to identify the parameters of the individual impedance blocks is applied to a commercial lithium-ion cell (lithium nickel oxide). Validation in the time domain shows high accuracy of the model (RMSE < 1% at ambient temperature for SoCs between 20% and 80% and for all current rates allowed by the manufacturer) in estimating the voltage at the device's terminals, efficiency, power and energy density under different current rates.

Automotive Battery Modelling a nd Management

The estimation of vehicle battery performance is typically addressed by testing the battery under specific operation conditions by using a model to represent the test results. Approaches for representing test results range from simple statistical models to neural networks to complex, physics-based models. Basing the model on test data could be problematical when testing becomes impractical with many years life time tests. So, real time estimation of battery performance, an important problem in automotive applications, falls into this area. In vehicles it is important to know the state of charge of the batteries in order to prevent vehicle stranding and to ensure that the full range of the vehicle operation is exploited. In this paper, several battery models have studied including analytical, electrical circuits, stochastic and electrochemical models. Valve Regulated Lead Acid “VRLA” battery has been modelled using electric circuit technique. This model is considered in the proposed Battery Monitoring System “BMS”. The proposed BMS includes data acquisition, data analysis and prediction of battery performance under a hypothetical future loads. Based on these criteria, a microprocessor based BMS prototype had been built and tested in automotive Lab,. The tests show promising results that can be used in industrial applications

A roadmap to understand battery performance in electric and hybrid vehicle operation

This work attempts to bridge laboratory and real-life battery testing data with a comprehensive analysis to provide a coherent approach for a realistic model to simulate battery performance, including life prediction. From electric vehicle field-testing results, we explain how to handle real-life data through driving cycle analysis to establish a scheme of “building blocks” that can be validated by test results obtained in the laboratory. We also show that a simple battery model can be built upon laboratory test data and validated by real-life duty cycles, therefore deriving a more realistic understanding and prediction of battery performance.