In this paper, a more accurate battery state of charge (SOC) estimation method for electric drive vehicles is developed based on a nonlinear battery model and an extended Kalman filter (EKF) supported by experimental data. A nonlinear battery model is constructed by separating the model into a nonlinear open circuit voltage and a two-order resistance-capacitance model. EKF is used to eliminate the measurement and process noise and remove the need of prior knowledge of initial SOC. A hardware-in-the-loop test bench was built to validate the method. The experimental results show that the proposed method can estimate the battery SOC with high accuracy.

With the development of electric drive vehicles (EDVs), the state-of-charge (SOC) estimation for lithium-ion (Li-ion) batteries has become increasingly more important. Based on the analysis of some of the most popular model-based SOC estimation methods, the proportional-integral (PI) observer is proposed to estimate the SOC of lithium-ion batteries in EDVs. The structure of the proposed PI observer is analyzed, and the convergence of the estimation method with model errors is verified. To demonstrate the superiority and compensation properties of the proposed PI observer, the simple-structure RC battery model is utilized to model the Li-ion battery. To validate the results of the proposed PI-based SOC estimation method, the experimental battery test bench is established. In the validation, the urban dynamometer driving schedule (UDDS) drive cycle is utilized, and the PI-based SOC estimation results are found to agree with the reference SOC, generally within the 2% error band for both the known and unknown initial SOC cases.

This paper focuses on building an efficient, online, and intelligent energy management controller to improve the fuel economy of a power-split plug-in hybrid electric vehicle (PHEV). Based on a detailed powertrain analysis, the battery current can be optimized to improve the fuel economy using dynamic programming (DP). Three types of drive cycles, i.e., highway, urban, and urban (congested), are classified, and six typical drive cycles are analyzed and simulated to study all the driving conditions. The online intelligent energy management controller is built, which consists of two neural network (NN) modules that are trained based on the optimized results obtained by DP methods, considering the trip length and duration. Based on whether the trip length and duration are known or unknown, the controller will choose the corresponding NN module to output the effective battery current commands to realize the energy management. Numerical simulation shows that the proposed controller can improve the fuel economy of the vehicle.

In this paper, the electric field resonance (EFR) method, similar to the four-coil configuration of the magnetic field resonance wireless power transfer, is proposed for the capacitive coupling power transfer. The characteristics of the proposed method are derived and analyzed. With the EFR method, not only unity power factor for the power source is achieved, but also high power factor and low reactive power for the capacitive coupling stage are achieved. Effective power transfer is realized by the EFR method. Based on the proposed method, a dynamic charging concept for railway vehicles is then proposed. A prototype powering system is designed and built to prove the validity of the proposed method. Analytical, simulation, and experimental results are given and compared. A 23-cm model vehicle is put on a 150-cm track. It is shown that about 700-W power is transferred through a 24-pF coupling capacitor. The proposed method reaches 91% dc–dc overall efficiency at switching frequency 2 MHz.