In developing battery management systems, estimating state-of-charge (SOC) is important yet challenging. Compared with traditional SOC estimation methods (eg, the ampere-hour integration method), extended Kalman filter (EKF) algorithm does not depend on the initial value of SOC and has no accumulated error, which is suitable for the actual working condition of electric vehicles. EKF is a model-based algorithm; the accuracy of SOC estimated by this algorithm was greatly influenced by the accuracy of battery model and model parameters. The parameters of battery change with many factors and exhibit strong nonlinearity and time variance. Typical EKF algorithm approximates battery as a linear, timeinvariant system; however, this approach introduces estimation errors. To minimize such errors, previous studies have focused on improving the accuracy of identifying battery parameters. Although studies on battery model with timevarying parameters have been carried out, few have studied the combination of time-varying battery parameters and EKF algorithm. A SOC estimation method that combines time-varying battery parameters with EKF algorithm is proposed to improve the accuracy of SOC estimation. Battery parameter data were obtained experimentally under different temperatures, SOC levels, and discharge rates. The results of parameter identification are made into a data table, and the battery parameters in the EKF system matrix are updated by looking up the data in the table. Simulation and experimental results shown that, average error of SOC estimated by the proposed algorithm is 2.39% under 0.9 C constant current discharge and 2.4% under 1.3 C, which is 1.91% and 2.35% lower than that of EKF algorithm with fixed battery parameters. Under intermittent discharge with constant current (1.1 C) and capacity (10%), the average error of SOC estimated by the proposed algorithm is 1.4%, which is 0.3% lower than that of EKF algorithm with fixed battery parameters. The average error of SOC estimated by the proposed algorithm under the New European Driving Cycle (NEDC) is 1.6%, which is 0.2% lower than that of EKF algorithm with fixed battery parameters. Relative to the EKF algorithm with fixed battery parameters, the proposed EFK algorithm with timevarying battery parameters yields higher accuracy.

P2.5 plug-in hybrid electric vehicles (P2.5-PHEVs) exhibit high transmission efficiencies and no power interruption in the shifting and mode switching process; thus, they have broad application prospects. The power transmission in a PHEV under pure electric startup does not entail the clutch, and the initial torque of the motor is often set under the condition that the maximum allowable slope should not lead to backward sliding, which leads to the problems of excessive jerk in small-slope startup and catapulting when startup occurs downhill. The optimal method is to set different initial torques according to different gradients; however, the vehicle would still be in the pre-startup stage, making it impossible to estimate the slope using a dynamic method. In view of the foregoing problems, according to an analysis of pure electric startup dynamics, a slope-memory-based strategy for estimating and storing the slope during the vehicle movement before parking is proposed. During startup, the initial torque is set according to the memorized slope. In the processes of startup and acceleration, the driver's startup intention is identified, and different jerk control targets are set. The torque of the acceleration process is controlled according to the set initial torque and jerk target. Simulation results indicate that the maximum jerk of the proposed strategy is reduced by 23.6% for startup on a 5% ramp and by 57.5% for startup at 15% downhill; thus, the strategy mitigates the problems of the excessive jerk and catapult for startup on a small slope.