A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency

Qiaohua Fang,Xuezhe Wei,Haifeng Dai

doi:10.3390/en12060987

Abstract

The remaining discharge energy prediction of the battery pack is an important function of a battery management system. One of the key factors contributing to the inaccuracy of battery pack remaining discharge energy prediction is the inconsistency of the state and model parameters. For a batch of lithium-ion batteries with nickel cobalt aluminum oxide cathode material, after analyzing the characteristics of battery model parameter inconsistency, a “specific and difference” model considering state of charge and R0 inconsistency is established. The dual time-scale dual extended Kalman filter algorithm is proposed to estimate the state of charge and R0 of each cell in the battery pack, and the remaining discharge energy prediction algorithm of the battery pack is established. The effectiveness of the state estimation and remaining discharge energy prediction algorithm is verified. The results show that the state of charge estimation error of each cell is less than 1%, and the remaining discharge energy prediction error of the battery pack is less than 1% over the entire discharge cycle. The main reason which causes the difference between the “specific and difference” and “mean and difference” models is the nonlinearity of the battery’s state of charge - open circuit voltage curve. When the nonlinearity is serious, the “specific and difference” model has higher precision.

Highlights

Compared with traditional internal-combustion vehicles, battery electric vehicles (BEVs) still have disadvantages, such as limited driving range, long charging time, insufficient charging infrastructure construction, and high prices
The results show that the state of charge estimation error of each cell is less than 1%, and the remaining discharge energy prediction error of the battery pack is less than 1% over the entire discharge cycle
The dual time-scale dual extended Kalman filter algorithm was proposed to estimate the state of charge and R0 of each cell in the battery pack, and the remaining discharge energy prediction algorithm of the battery pack was established based on the state estimation results

Summary

Introduction

Compared with traditional internal-combustion vehicles, battery electric vehicles (BEVs) still have disadvantages, such as limited driving range, long charging time, insufficient charging infrastructure construction, and high prices. The poor range estimation accuracy deteriorates the anxiety within the low mileage range, as the drivers are fearful of fully depleting a BEV’s battery in the middle of a trip. BMSs commonly use the state of charge (SOC), which refers to the ratio of the available current capacity to the nominal capacity, of the battery pack directly to estimate the remaining range [2]. The reason is that the Energies 2019, 12, 987; doi:10.3390/en12060987 www.mdpi.com/journal/energies

Methods

Results

Conclusion