Optimization of Control Strategy for Orderly Charging of Electric Vehicles in Mountainous Cities

Abstract

In light of the increasing number of electric vehicles (EV), disorderly charging in mountainous cities has implications for the stability and efficient utilization of the power grid. It is a roadblock to lowering carbon emissions. EV aggregators are a bridge between EV users and the grid, a platform to achieve energy and information interoperability, and a study of the orderly charging of EVs to reach carbon emission targets. As for the objective function, the EV aggregator considers the probability of EV charging access in mountainous cities, the SOC expectation of EV users, the transformer capacity constraint, the charging start time, and other constraints to maximize revenue. Considering the access probability of charging for users in mountainous cities, the optimized Lagrange relaxation method is used to solve the objective function. The disorderly charging, centralized optimized charging, and decentralized optimized charging modes are investigated using simulation calculations. Their load profiles, economic benefits, and computational efficiency are compared in three ways. Decentralized optimal charging using the Lagrange relaxation method is shown to be 50% more effective and to converge 279% faster than centralized optimal charging.

Research on gear management strategy and verification method for pure electric vehicle

“…In order to prevent the driver from misoperation, the gear of the shifter is judged based on the vehicle speed and the time when the gear button is pressed. The driver presses the D key to set the shift position to D; If the vehicle speed is less than 2 km/h, the driver presses the P key to set the shift position to P; If the vehicle speed is ≥2Km/h, the driver presses the P key, and when it lasts for 340ms, the shift position is set to P; If the vehicle speed is less than 25 km/h, the driver presses the R key to set the shift position to R; If the vehicle speed is more than or equal to 25 km/h, the driver presses the R key for 100ms, and then sets the shift position to R; If the vehicle speed is less than 25 km/h, the driver presses the N-gear key to set the gear shift position to N; If the vehicle speed is more than or equal to 25 km/h, the driver presses the N-gear button for 350ms, and then sets the shift position to N [4] ; When the driver inputs the gear requirements of multiple operations at the same time, and determines multiple gears at the same time, if it contains the P gear, it will use the P gear, and if it does not, it will keep the previous time value; If the driver inputs the gear requirements of multiple operations in turn, the final shift operation gear is adopted.…”

“…Gear management strategy needs to be based on the driver's gear shifting operation and current vehicle state comprehensive judgment to get the final gear shifting instruction [3] . Currently, the existing gear management strategies mainly focus on avoiding driver's misoperation to make gear shift decisions, such as speed restriction [4] , pedal position restriction or vehicle state restriction when it is necessary to switch between D and R gears [5] or focus on gearshift-based control strategy [6] [7] .…”

“…Based on the developed Test cases, the Signal Builder is used to build test data [4] , and the control strategy model is verified by Simulink Test tool. Analyze the test results one by one, and optimize the control strategy and model or test cases based on the analysis results until the formulated strategy meets the target requirements, the model and strategy definitions are consistent, and all test cases pass the test.…”

Status and Development of Research on Orderly Charging and Discharging of Electric Vehicles

“…In addition, the traditional convex optimization algorithm is a single-objective optimization, which requires additional improvements to be applied to the ordered charging of electric vehicles. The authors in [59] transformed the ordered charging problem by convex optimization and solved the objective function using the Lagrangian relaxation method. The user charging cost is reduced, and the convergence speed of the algorithm is significantly improved.…”

A quantitative assessment method for electric vehicle acceptance capacity considering orderly charging

“…The literature [3] studied the impact of EV charging on the distribution network and analyzed the potential of EVs to respond to policy charging. The literature [4], on the other hand, conducted a reliability assessment of PHEV access under generation and transmission, considering the impact of line failure rate and charging policy factors on the reliability level, respectively. However, the above literature is limited to qualitative analysis and lacks quantitative assessment of the size of EVs that the system can withstand.…”