Cheng Yu

Accurate estimation of the state of energy (SOE) in lithium-ion batteries is crucial for determining the output power and driving range of electric vehicles. However, in series-connected battery packs, inconsistencies among individual cells pose significant challenges for precise SOE estimation. This issue is particularly pronounced for lithium iron phosphate (LFP) batteries. Their relatively flat open-circuit voltage (OCV) curve makes the classic method of directly weighting the SOE of representative cells—commonly used for ternary batteries—ineffective. This is because the traditional method relies heavily on a linear relationship between the SOE and the voltage, which is not present in LFP batteries. To address this challenge, a novel SOE estimation approach based on the online construction of the battery pack’s comprehensive OCV curve is proposed in this paper. In this new approach, the weighting of representative cells shifts from a result-oriented mode to a key-parameter-oriented mode. By adopting this mode, the whole pack’s comprehensive OCV can be obtained training free and the pack’s SOE can be estimated online within an equivalent circuit model framework. The experimental results demonstrate that the proposed method effectively controls the SOE estimation error within 3% for series battery packs composed of cells with varying degrees of aging.

To ensure the accuracy of state of charge (SOC) and state of health (SOH) estimation for battery packs while minimizing the amount of pre-experiments required for aging modeling and the scales of computation for online management, a decisive-cell-based estimation method with training-free characteristic parameters and a dynamic-weighted estimation strategy is proposed in this paper. Firstly, to reduce the computational complexity, the state estimation of battery packs is summed up to that of two decisive cells, and a new selection approach for the decisive cells is adopted based on the detection of steep voltage changes. Secondly, two novel ideas are implemented for the state estimation of the selected cells. On the one hand, a set of characteristic parameters that only exhibit local curve shrinkage with aging is chosen, which keeps the corresponding estimation approaches away from training. On the other hand, multiple basic estimation approaches are effectively combined by their respective dynamic weights, which ensures the estimation can maintain a good estimation accuracy under various load profiles. Finally, the experimental results show that the new method can quickly correct the initial setting deviations and have a high estimation accuracy for both the SOC and SOH within 2% for a series battery pack consisting of cells with obvious inconsistency.