Research on Double Energy Fuzzy Controller of Electric Vehicle Based on Particle Swarm Optimization of Multimedia Big Data

Research on Double Energy Fuzzy Controller of Electric Vehicle Based on Particle Swarm Optimization of Multimedia Big Data

Xiaokan Wang (Beijing Jiaotong University, Beijing, China)
DOI: 10.4018/IJMCMC.2017070103
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The pure electric vehicles have the problems of short driving range, poor acceleration performance and battery performance, this paper presents a novel double - energy fuzzy control algorithm for battery-supercapacitor based on particle swarm optimization (PSO). The proposed algorithm can avoid falling into local optimum and being over reliance on prior knowledge by using the swarm intelligence global optimization and evolutionary operation. The simulation results show that this method can improve the vehicle performances in the large extent and verify the effectiveness of the control strategy. It is very important for improving the development and research level and promoting industrialization process of pure electric vehicles.
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The application of electric vehicle effectively solves the sustainable development problems of energy and environment, so its application prospect is broad. But application of electric vehicle especially pure electric vehicle encountered a power battery problem which reflected in two aspects: (1) the energy ratio of power battery is not high, so that it will directly affect the driving distance of electric vehicle, and the too high price is directly affect the initial cost of the electric vehicle; (2) the battery performance is poor, and low service life affects use cost of electric vehicle.

The battery performance of electric vehicle how to display, with the exception of itself performance of the battery module, and has closely relation of battery power management system. The battery energy management system with complete function which function will more prominent, especially when the battery module quality is not ideal conditions. With the help of battery energy management system, the battery module performance can be fully displayed, and it could reduce the fault of battery module and extend the service life of battery module, and increase the security sense using electric vehicle. Therefore, the application of electric vehicle battery energy management system has paid well attention of vehicle designers and users.

The development of electric vehicles in China is facing strong policy support and market opportunities, the related enterprises of the domestic are also accelerating the key technologies development of electric vehicle management system. In big data era, the data value cannot be ignored. According to the reports, when the system collecting the big data, the advanced application function menu will massive data mining and analysis from the 3 dimensions of the energy saving and emission reduction, user behavior analysis, electric vehicle charging facilities for electric company asset utilization, it can provide a scientific basis for the decision-making of electric vehicle business model selection and planning and become effective support technology for the development of electric vehicles. The system can see that each month of electric vehicle mileage, reduce carbon emissions and fuel real-time data, and implement the analysis of energy saving and emission reduction; it can see the different user vehicle of the charging habits and charging equipment, and provide a scientific basis for the rational layout and power effectively matching. After big data accessing, the system will build more in line with the development trend of electric vehicle in accordance with the power system reform and electric vehicle technology trends.

The paper Laamari et al. (2015) used the particle swarm optimization to solve the carton heterogeneous vehicle routing problem. To improve the performance of the PSO, a self-adaptive inertia weight and a local search strategy are used. The model and the algorithm are illustrated with two test examples. The results show that the proposed PSO is an effective method to solve the multi-depot vehicle routing problem, and the carton heterogeneous vehicle routing problem with a collection depot. Belmecheri et al. (2013) presented Particle Swarm Optimization with a local search to describe a vehicle routing problem with mixed Linehaul and Backhaul customers. (Chen et al., 2017; Chen et al., 2016) proposed a novel optimal power management approach for plug-in hybrid electric vehicles against uncertain driving conditions. To optimize the threshold parameters of the rule-based power management strategy under a certain driving cycle, the particle swarm optimization algorithm was employed, and the optimization results were used to determine the optimal control actions. To better implement the power management strategy in real time, a driving condition recognition algorithm was proposed to identify real-time driving conditions through a fuzzy logic algorithm. To adjust the thresholds of the rule-based strategy adaptive under uncertain driving cycles, a dynamic optimal parameters algorithm has been further established accordingly, and it is helpful for avoiding the problem that the thresholds of the rule-based strategy are very sensitive to the driving cycles. Cheng et al. (2016) proposed a particle swarm optimization based on power dispatch algorithm is to deal with the energy management problem of the hybrid generation system. To address power dispatch problems, a roulette wheel re-distribution mechanism is proposed. With this re-distribution mechanism, unbalanced power can be reallocated to more superior element and the searching diversity can be preserved.

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