Ambient temperature is a significant factor that influences the characteristics of lithium-ion batteries, which can produce adverse effects on state of charge (SOC) estimation. In this paper, an integrated SOC algorithm that combines an advanced ampere-hour counting (Adv Ah) method and multistate open-circuit voltage (multi OCV) method, denoted as “Adv Ah + multi OCV”, is proposed. Ah counting is a simple and general method for estimating SOC. However, the available capacity and coulombic efficiency in this method are influenced by the operating states of batteries, such as temperature and current, thereby causing SOC estimation errors. To address this problem, an enhanced Ah counting method that can alter the available capacity and coulombic efficiency according to temperature is proposed during the SOC calculation. Moreover, the battery SOCs between different temperatures can be mutually converted in accordance with the capacity loss. To compensate for the accumulating errors in Ah counting caused by the low precision of current sensors and lack of accurate initial SOC, the OCV method is used for calibration and as a complement. Given the variation of available capacities at different temperatures, rated/non-rated OCV–SOCs are established to estimate the initial SOCs in accordance with the Ah counting SOCs. Two dynamic tests, namely, constant- and alternated-temperature tests, are employed to verify the combined method at different temperatures. The results indicate that our method can provide effective and accurate SOC estimation at different ambient temperatures.

This study describes an online estimation of the model parameters and state of charge (SOC) of lithium iron phosphate batteries in electric vehicles. A widely used SOC estimator is based on the dynamic battery model with predeterminate parameters. However, model parameter variances that follow with their varied operation temperatures can result in errors in estimating battery SOC. To address this problem, a battery online parameter estimator is presented based on an equivalent circuit model using an adaptive joint extended Kalman filter algorithm. Simulations based on actual data are established to verify accuracy and stability in the regression of model parameters. Experiments are also performed to prove that the proposed estimator exhibits good reliability and adaptability under different loading profiles with various temperatures. In addition, open-circuit voltage (OCV) is used to estimate SOC in the proposed algorithm. However, the OCV based on the proposed online identification includes a part of concentration polarization and hysteresis, which is defined as parametric identification-based OCV (OCV_PI). Considering the temperature factor, a novel OCV–SOC relationship map is established by using OCV_PI under various temperatures. Finally, a validating experiment is conducted based on the consecutive loading profiles. Results indicate that our method is effective and adaptable when a battery operates at different ambient temperatures.

Owing to cell‐to‐cell variations in battery pack, it is hard to manage cells of the battery pack safely and effectively. As a result, the battery pack performance is rapidly degraded, which in turn spread the differences in individual cells. Ambient temperature is a significant factor that influences the characteristics of lithium‐ion battery and cell variations in the pack. This study tries to put effort in researching the temperature characteristics of cells series battery pack. The battery model parameters identification tests are designed to analyse the inconsistency characteristics of cells [such as open‐circuit voltage (OCV), ohmic and polarisation resistances and polarisation capacitance] under various ambient temperatures. The results indicate that ohmic and polarisation resistances are most significantly increased as the temperature decreases, whereas OCV is opposite. The variation of cells inconsistency characteristics is obvious along with the temperature change. Comprehensive above factors, the change rate of battery pack capacity is faster than that of a single cell with the drop in temperature.

Owing to different temperature gradients, charge/discharge rates, and coulomb efficiencies, various releasable capacities of each cell have significant impacts on a pack capacity. Therefore, the equalisation system is an essential part in a battery pack, especially for a high‐power application such as electric vehicle. In this study, the authors first review several traditional equalisation strategies. Subsequently, a new equalisation strategy (ES) based on the real ‘state’ of a battery is proposed. They further establish the destination state‐of‐charge versus open‐circuit voltage (SOC–OCV) cluster which is the target of the ES. Moreover, not only each SOC in serially connected battery cells, but also the SOC error according to the destination SOC–OCV cluster can be estimated. To avoid over‐equalisation, the SOC error caused by the dispersion of OCVs is taken into account during the calculation of equalisation SOCs. A battery pack with five lithium iron phosphate cells in series is employed to verify the method, and results show that the capacity of battery pack is increased by 11% with equalisation.