Abstract
Measurements such as current and terminal voltage that are typically used to determine the battery’s state of charge (SOC) are augmented with measured force associated with electrode expansion as the lithium intercalates in its structure. The combination of the sensed behavior is shown to improve SOC estimation even for the lithium ion iron phosphate (LFP) chemistry, where the voltage–SOC relation is flat (low slope) making SOC estimation using measured voltage difficult. For the LFP cells, the measured force has a non-monotonic F–SOC relationship. This presents a challenge for estimation as multiple force values can correspond to the same SOC. The traditional linear quadratic estimator can be driven to an incorrect SOC value. To address these difficulties, a novel switching estimation gain is used based on determining the operating region that corresponds to the actual SOC. Moreover, a drift in the measured force associated with a shift of the cell SOC–expansion behavior over time is addressed with a bias estimator for the force signal. The performance of Voltage-based (V) and Voltage and Force-based (V&F) SOC estimation algorithms are then compared and evaluated against a desired ± 5 % absolute error bound of the SOC using a dynamic stress test current protocol that tests the proposed estimation scheme across wide range of SOC and current rates.
Highlights
The primary function of the battery management system (BMS) is to provide an accurate state of charge (SOC) estimation
We demonstrate the improvement in the SOC estimation of LFP batteries by using mechanical in addition to electrical measurements that can be implemented in packs or modules of both hard encased and pouch cells [21]
The Voltage and Force-based (V&F) Bias observer exhibits lower root mean square error (RMSE), faster time convergence to the denoted SOC estimation error bound (EEB), and reduced maximum absolute SOC error, as shown in Table 6 compared to V observer
Summary
The primary function of the battery management system (BMS) is to provide an accurate state of charge (SOC) estimation. The SOC represents the amount of charge in ampere-hours (Ah) remaining in a cell divided by its total capacity [1,2]. The BMS traditionally uses current, voltage, and sometimes temperature measurements to estimate the SOC to plan future actions and to prevent over-charging or discharging of cells. Manufacturers provide conservative estimates of remaining energy, since an overestimation of SOC can leave the vehicle stranded. In the case of unmanned air vehicles (UAV), overestimation of SOC might prevent the vehicle from safe landing, since landing maneuvers require very high power, which typically cannot be achieved at very low SOC levels [3]. Underestimating SOC, on the other hand, wastes valuable resources and adds cost and weight to the vehicles, which is critical for robotic platforms
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