An Adaptive Estimation Approach based on Fisher Information to Overcome the Challenges of LFP Battery SOC Estimation

TL;DR

This paper introduces an adaptive Fisher information-based method for real-time SOC estimation in LFP batteries, effectively addressing flat OCV-SOC challenges and real-world conditions, outperforming existing techniques.

Contribution

The paper proposes a novel adaptive estimation approach using Fisher information fusion and a 3D OCV-H-SOC map, enhancing robustness and accuracy in SOC estimation under various conditions.

Findings

Outperforms unscented Kalman filter, LSTM, and transformer methods.

Effective under flat OCV-SOC zones and real-world disturbances.

Validated with multiple driving profiles showing superior accuracy.

Abstract

Robust and Real-time State of Charge (SOC) estimation is essential for Lithium Iron Phosphate (LFP) batteries, which are widely used in electric vehicles (EVs) and energy storage systems due to safety and longevity. However, the flat Open Circuit Voltage (OCV)-SOC curve makes this task particularly challenging. This challenge is complicated by hysteresis effects, and real-world conditions such as current bias, voltage quantization errors, and temperature that must be considered in the battery management system use. In this paper, we proposed an adaptive estimation approach to overcome the challenges of LFPSOC estimation. Specifically, the method uses an adaptive fisher information fusion strategy that adaptively combines the SOC estimation from two different models, which are Coulomb counting and equivalent circuit model-based parameter identification. The effectiveness of this strategy is rationalized by the information richness excited by external cycling signals. A 3D OCV-H-SOC map that captures the relationship between OCV, hysteresis, and SOC was proposed as the backbone, and can be generalizable to other widely adopted parameter-identification methods. Extensive validation under ideal and real-world use scenarios, including SOC-OCV flat zones, current bias, voltage quantization errors, low temperatures, and insufficient current excitations, have been performed using 4 driving profiles, i.e., the Orange County Transit Bus Cycle, the California Unified Cycle, the US06 Drive Cycle, and the New York City Cycle, where the results demonstrate superiority over the state-of-the-art unscented Kalman filter, long short-term memory networks and transformer in all validation cases.