Battery management system (BMS) is an important part of electric vehicle that can improve the efficiency of battery and ensure the safety during operating process. State of charge (SOC) estimation is one of kernel technology of BMS. High precision of SOC estimation can not only increase service life of battery, but also enhance energy utilization efficiency and cut down the operational cost. Based on fully understand working principle of battery, three-order RC equivalent circuit is chose to simulate the external characteristics. Introduce the working theory of that combination of extended Kalman filter (EKF) and open circuit voltage (OCV), and the whole recursive algorithm of estimation SOC, using the OCV-SOC look-up table to solve the problem of initial value of SOC to a certain degree. The hardware circuit and software of BMS are designed. Hardware circuit consists of current measurement circuit, voltage measurement circuit, temperature measurement circuit and equalization circuit. The software is designed by C as the development language, the Microsoft Visual Studio 2008 as development environment and SQL Server 2008 as database management system. The experiments are carried out after the experimental platform of system hardware and software is constructed. The system realizes the real-time online monitoring of BMS, SOC estimation and etc. The result verifies the reliability and feasibility. The precision requirement of SOC estimation by using EKF algorithm and OCV-SOC look-up table meets the national standard for electric vehicle.

To overcome the limitations of traditional integer-order models, which fail to accurately capture the dynamic behavior of lithium-ion batteries, and to improve the insufficient accuracy of state of charge (SOC) and state of health (SOH) collaborative estimation, this study proposes a cooperative estimation framework based on a fractional-order model. First, a fractional-order second-order RC equivalent circuit model is established, and the whale optimization algorithm is applied for offline parameter identification to improve model accuracy. Second, a strong tracking strategy is introduced into the improved unscented Kalman filter to address the convergence speed issue under inaccurate initial SOC conditions. Meanwhile, the extended Kalman filter is employed for SOH estimation and online parameter identification. Furthermore, a multi-time-scale collaborative estimation algorithm is proposed to enhance overall estimation accuracy. Experimental results under three dynamic operating conditions driving cycles demonstrate that the proposed method effectively solves the SOC/SOH collaborative estimation problem, achieving a mean SOC estimation error of 0.45% and maintaining the SOH estimation error within 0.25%.

During the planning and construction of electric vehicle charging stations (EVCSs), consideration of the long-term operating revenue loss for investors is often lacking. To address this issue, this study proposes an EVCS planning method that takes into account the potential loss of long-term operating revenues associated with charging facilities. First, the method combines the Bass model with electric vehicle (EV) user travel characteristics to generate a charging load dataset. Then, the cost of profit loss—which represents the EVCS utilization rate—is incorporated into the construction of the objective function. Additionally, a parallel computing method is introduced into the solution algorithm to generate the EVCS planning scheme. Finally, the cost-to-profit ratio of the EVCSs is used as a filtering condition to obtain the optimal EVCS planning scheme. The results show that the EVCS planning scheme considering the profit loss reduces the annual comprehensive cost by 24.25% and 16.93%, and increases the net profit by 22.14% and 24.49%, respectively, when compared to the traditional planning scheme under high and low oil prices. In particular, the charging station strategy proposed in this study has the best effect in the case of high oil prices.