Lithium-Ion Battery Online Rapid State-of-Power Estimation under Multiple Constraints

Shun Xiang,Pengkai Zhou,Ruisen Huang,Feng Guo,Guangdi Hu

doi:10.3390/en11020283

Shun Xiang, Pengkai Zhou + Show 3 more

Open Access

https://doi.org/10.3390/en11020283

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Journal: Energies	Publication Date: Jan 24, 2018
Citations: 19	License type: CC BY 4.0

Affiliation: Southwest Jiaotong University, Pusan National University

Abstract

The paper aims to realize a rapid online estimation of the state-of-power (SOP) with multiple constraints of a lithium-ion battery. Firstly, based on the improved first-order resistance-capacitance (RC) model with one-state hysteresis, a linear state-space battery model is built; then, using the dual extended Kalman filtering (DEKF) method, the battery parameters and states, including open-circuit voltage (OCV), are estimated. Secondly, by employing the estimated OCV as the observed value to build the second dual Kalman filters, the battery SOC is estimated. Thirdly, a novel rapid-calculating peak power/SOP method with multiple constraints is proposed in which, according to the bisection judgment method, the battery’s peak state is determined; then, one or two instantaneous peak powers are used to determine the peak power during T seconds. In addition, in the battery operating process, the actual constraint that the battery is under is analyzed specifically. Finally, three simplified versions of the Federal Urban Driving Schedule (SFUDS) with inserted pulse experiments are conducted to verify the effectiveness and accuracy of the proposed online SOP estimation method.

Highlights

In recent years, the environmental pollution and the energy crisis have become more and more serious, resulting in conventional fuel vehicles being increasingly difficult to adapt to the development needs around the world [1]
The results of the experiment and Simulink indicate that the SOC based on the dual extended Kalman filtering (DEKF) method has a higher accuracy of 0.28% in root mean square error (RMSE) than the method based on the offline measured open-circuit voltage (OCV)-SOC relationship of 1.98% accuracy in RMSE; as the prediction time horizon increases from 10 s to 30 s, the ratio of the calculation time of the proposed method to the traditional method sharply decreases from 71.1% to
DEKFThe simulation and values the battery voltagein obtained based on the experimental test with of a 10s close-to-zero of the terminal power measured the pulses are regarded as the true peak power pulse the arebattery compared

Summary

Introduction

The environmental pollution and the energy crisis have become more and more serious, resulting in conventional fuel vehicles being increasingly difficult to adapt to the development needs around the world [1]. The BMS is designed to guarantee safe, reliable, and efficient battery operation and the major functions usually include state monitoring, state estimation, battery thermal management, and battery balancing, etc. The accurate estimation of various states of the battery, including state-of-charge (SOC), state-of-health (SOH), and state-of-power (SOP), is a priority [13,14,15,16,17,18,19,20,21]. The more accurate the battery model is, the more precise are the estimations of a battery’s states. G.L. High-performance battery-pack power estimation using a dynamic cell model.

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Discussion

Conclusion