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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.

Lithium-ion batteries have been applied in the new energy vehicles more and more widely. The inconsistency of battery cells imposes a lot of difficulties in parameter and state estimations. This paper proposes a new algorithm which can online identify the parameters of each individual battery cell accurately with limited increase of computational cost. An equivalent circuit battery model is founded and based on the RLS (recursive least squares) algorithm, an optimization algorithm with the construction of weight vectors is proposed which can identify the parameters of lithium battery pack considering inconsistency of single battery cell. Firstly, the average value of the parameters of the battery pack is identified with the traditional RLS algorithm. Then the ratios between the parameters of each battery cell can be deduced from the mathematical model of battery. These ratios are used to determine the weight vector of each parameter of individual battery cells.

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.

Alternating current (AC) heating is an efficient and homogeneous manner to warm Lithium-ion batteries (LIBs) up. The integrated design of AC heating combined with the motor drive circuit has been studied by many scholars. However, the problems of excessive heating frequency (>1kHz) and zeros torque output of the motor during the heating process have not been solved. High-frequency AC excitation may be detrimental to the battery because the effect of high-frequency AC excitation on the state of health of the battery is unknown. In addition, although the zero-torque output can be realized by controlling the q-axis current to zero, the torque ripple is still difficult to eliminate in a real-world application. To further solve the above problems, the motor’s neutral conductor is pulled out and connected to a large capacitor to increase the current amplitude of the AC heating at low frequencies.