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Parallel-connected modules have been widely used in battery packs for electric vehicles nowadays. Unlike series-connected modules, the direct state inconsistency caused by parameter inconsistency in parallel modules is current and temperature non-uniformity, thus resulting in the inconsistency in the speed of aging among cells. Consequently, the evolution pattern of parameter inconsistency is different from that of series-connected modules. Since it’s practically impossible to monitor each cell’s current and temperature information in battery packs, considering cost and energy efficiency, it’s necessary to study how the parameter inconsistency evolves in parallel modules considering the initial parameter distribution, topology design and working condition. In this study, we assigned cells of 18650 format into several groups regarding the degree of capacity and resistance inconsistency. Then all groups are cycled under different environmental temperature and current profile.

Range anxiety problem has always been one of the biggest concern of consumers for pure electric vehicles. Accurate driving range prediction is based on accurate lithium-ion battery pack SOC (State of Charge) estimation. In this article, a complete SOC estimation algorithm is proposed from cell level to battery pack level. To begin with, the equivalent circuit model (ECM) is applied as the model of battery cell. ECM parameters are identified every 10% SOC interval through genetic algorithm. The dual extended Kalman filtering (DEKF) algorithm is adopted for cell-level SOC and ohmic resistance R0 estimation. The estimation accuracy of cell SOC and R0 is verified under NEDC dynamic working condition. The cell-level SOC estimation error is below 1%. However, cell inconsistency can always result in inaccurate cell SOC estimation inside the battery pack. The impact of initial SOC inconsistency and internal resistance inconsistency between cells on battery pack SOC is specifically analyzed.