Fuzzy optimization of energy management for power split hybrid electric vehicle based on particle swarm optimization algorithm

Abstract

A dual planetary gear hybrid electric vehicle is introduced in the paper. The speed and torque relations between different components of the power split hybrid system are systematically established using the lever analogy. Based on the electric power balance, the influence of the ratio between the engine speed and output speed of the power coupling system on the hybrid electric vehicle powertrain transmission efficiency is revealed, and the transmission efficiency optimization strategy is further proposed. Since some engine operation points are distributed in the low-efficiency region, the engine torque control strategy is designed using fuzzy control algorithm. The fuzzy membership function and fuzzy rules are optimized by particle swarm optimization to reduce the bad influence of expert experience and knowledge on the derivation of optimal engine torque. The vehicle powertrain and the control strategy models are established based on AVL/ Cruise and MATLAB/Simulink platforms, and the joint simulation is conducted. Simulation results show that the proposed optimal energy management strategy can optimize the operation points of the engine. The final value of the battery state of charge is kept within a reasonable range, and the equivalent fuel consumption of the whole vehicle is reduced by 10.26% compared with that the transmission efficiency optimization control strategy.

An Improved Energy Management Strategy for 24t Heavy-Duty Hybrid Emergency Rescue Vehicle With Dual-Motor Torque Increasing

“…The global optimal control strategy can achieve the global optimization in offline states, which is widely studied at present. The most widely used intelligent optimization algorithms mainly include genetic algorithm [24][25][26], simulated annealing (SA) algorithm [27], particle swarm optimization (PSO) algorithm [14,[28][29][30][31][32], and other intelligent optimization algorithm strategies. In addition, the neural network, working condition recognition, machine learning and other technologies are used to design energy management strategies [33][34][35][36][37][38][39].…”

Intelligent Energy Management Systems for Electrified Vehicles: Current Status, Challenges, and Emerging Trends

“…The integration of optimization methods is not limited to deterministic rule-based approaches. Authors in [114] have optimized the rule set and membership functions of a fuzzy logic method by the PSO algorithm to yield a better fuel economy. Authors in [114] have also compared the performance of the fuzzy optimized method with that of a deterministic rule-based approach.…”

“…Authors in [114] have optimized the rule set and membership functions of a fuzzy logic method by the PSO algorithm to yield a better fuel economy. Authors in [114] have also compared the performance of the fuzzy optimized method with that of a deterministic rule-based approach. The optimized method with the PSO algorithm leads to 10.26% reduction in fuel consumption.…”

Real-time Energy Management Strategy for Oil-Electric-Liquid Hybrid System based on Lowest Instantaneous Energy Consumption Cost

“…Meanwhile, Yin et al proposed a dual-planetary hybrid electric vehicle as an object of engine torque control. This strategy can optimize the engine operating point while keeping the final battery state of charge (SOC) value within a reasonable range [4]. Although this type of energy management strategy has a simple structure and strong practicability, its advantages and disadvantages are easily affected by the experience of engineering personnel and the working conditions are poor.…”