With the development of battery technology, large-scale battery applications are increasing. In order to obtain a higher current and voltage level and improve the overall energy efficiency, batteries are connected in series and parallel. Bulk model is the most used model to simulate battery packs, and the simulation results of single cell are enlarged several times to represent a battery pack. But bulk model ignores inconsistency between the batteries, and when the current magnification is large, such as in the extreme fast charge scenario, this neglect cannot reflect the current distribution inside the battery pack. Our paper proposes a simplify algorithm based on equivalent circuit model, which applied for non-uniform series and parallel-connected batteries. This simplification can greatly improve simulation speed while maintaining accuracy under the consideration of inconsistency. More importantly, this algorithm applies to any complex circuit, regardless of how many series-parallel structure layers. With the proposed algorithm, Monte Carlo simulations were evaluated to obtain the influence of battery series and parallel number and normal distribution parameters on current distribution. Simulation results show that an increase in the number of series will reduce the inconsistency of the current distribution, and an increase in the number of parallel and an increase in the relative standard deviation of the normal distribution of the battery parameters will increase the inconsistency of the current distribution.

Accurate state of energy (SOE) estimation of the battery pack is very important for the electric vehicle's driving range estimation, which is still a very challenging problem under actual vehicle application conditions at present. This paper proposes a method for estimating the SOE of the battery pack based on prediction, taking into account the future voltage and temperature changes of the battery pack. Firstly, a novel definition of the battery pack SOE is proposed, which considers the inconsistency of the battery pack. Secondly, the thermo-electric lumped parameter equivalent circuit model of a single cell in the battery pack is applied. The electrical parameters are identified using the forgetting factor recursive least-squares algorithm, and the extended Kalman filter is used to estimate the SOC of each cell. Finally, the future current, voltage and temperature response of the cell in the battery pack are accurately predicted and iterative calculations are performed to get SOE under the condition that the future output power of the battery pack is predicted by the Markov chain model method. The effectiveness of the proposed method is verified under dynamic conditions at different temperatures. The results show that the above method has high precision and good robustness, and can meet the needs of actual vehicle applications.