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

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.

An Approach to State of Charge Estimation of Lithium-Ion Batteries Based on Recurrent Neural Networks with Gated Recurrent Unit

“…Traditionally, two widely used SOC estimation methods are the ampere-hour counting method [3] and the open-circuit voltage method [4]. Generally speaking, the former requires the battery to remain resting for a long time to reach balance in order to obtain the open circuit voltage (OCV) versus the SOC curve.…”

State of Charge Estimation of a Lithium Ion Battery Based on Adaptive Kalman Filter Method for an Equivalent Circuit Model

“…(3) the ampere-hour integration method [8]; (4) the Kalman filter method [9]; (5) the deep learning algorithm [10]; (6) the neural network method [11]; and (7) hybrid techniques [9].…”

“…The hybrid technique means an SOC estimation method that combines various SOC estimation methods appropriately. For example, Liu et al designed an SOC estimation method combining the adaptive extended Kalman filter method (AEKF) and the ampere-hour integration method together [9]. The experimental results show that the accuracy of this method is relatively high and the calculation time is short, and the practicability is better.…”

“…The greatest problem with this method at present is that the cost of computing is still very high. Still with the article of Liu as an example [9], the method proposed in this paper needs to construct the adaptive extended Kalman filter method and the ampere-time integral method at the same time, which undoubtedly brings a great burden to the BMS. However, finding the right hybrid method is still one of the development directions for the future SOC estimation.…”

“…The most challenging issue in the SOC estimation is the trade-off between balance accuracy and computational cost [9]. In general, the more complex the algorithm, the higher the accuracy, but the higher the cost.…”