Accurate and Efficient Estimation of Lithium-Ion Battery State of Charge with Alternate Adaptive Extended Kalman Filter and Ampere-Hour Counting Methods

Zhongxiao Liu,Zhe Li,Laisuo Su,Jianbo Zhang,Hao Ge

doi:10.3390/en12040757

Abstract

State of charge (SOC) estimation is a key issue in battery management systems. The challenge lies in balancing the trade-off between accuracy and computation cost. To this end, we propose an alternate method by combining the ampere-hour integral (AHI) method which has low computation cost, and the adaptive extended Kalman filter (AEKF) method, which has high accuracy. The technical viability of this alternate method is verified on a LiMnO2-LiNiO2 battery module with a nominal capacity of 130 Ah under the New European Driving Cycle (NEDC) condition. Drifts in current and voltage measurement are considered. The experimental results show that the absolute SOC error using the AHI method monotonously increases from 0% to 7.2% with the computation time of 10 s while the calculation time is obtained on a ThinkPad E450 PC with an Intel Core i7-5500U CPU @2.40 GHz and 16.0 GB RAM. The absolute SOC error of the AEKF method maintains within 3.5% with the computation time of 49 s. Therefore, the alternate method almost maintains the same SOC accuracy compared to the AEKF method which reduces the maximum absolute SOC error by 50% compared to the AHI method. Therefore, the alternate method almost has the same computation time compared with the AHI method which reduces the computation time by nearly 75% compared to the AEKF method.

Highlights

Lithium-ion batteries are widely used in electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) owing to their high energy density, high power density, low self-discharge rate, and long cycle life [1,2]
When the current is measured that the current is set as true when the voltage is added with different drift errors, and the voltage is set with drift errors, the state of charge (SOC) estimation errors of the ampere-hour integral (AHI) method grow in the process even with the true as true when the current is multiplied different errors
The results show that the adaptive extended Kalman filter (AEKF) and alternate

Summary

Introduction

Lithium-ion batteries are widely used in electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) owing to their high energy density, high power density, low self-discharge rate, and long cycle life [1,2]. The energy and power of a single lithium ion cell is far from being sufficient for vehicular use, a multitude of cells are connected in parallel or in series as a battery module, and tens or hundreds of modules are connected in series as a battery pack in EVs and PHEVs [3]. Energies 2019, 12, 757 systems (BMS) to monitor and adjust every battery module’s state parameter, such as voltage, current, temperature and SOC. Among these parameters, estimating every battery module’s SOC has a high priority and still remains a challenge

Methods

Results

Conclusion