Study on Fuzzy Energy Management Strategy of Parallel Hybrid Vehicle Based on Quantum PSO Algorithm

Abstract

A fuzzy energy management strategy was designed for the single-axle parallel hybrid electric vehicles, and then the quantitative factor of the fuzzy logic controller was optimized by the quantum PSO algorithm under the Matlab platform based on the equivalent fuel economy. Then a comparison test about the energy management before and after the optimization was carried out based on the secondary development of Advisor which proves that the energy management strategy optimized by the quantum PSO algorithm can improve the SOC of the battery pack by 18% when the VAIL2NREL cycle finished while the power of the vehicles nearly remain the same. What's more, the optimized strategy can make the engine and the motor works in the high efficient area for most of the time which can improve the recycling rate of the energy and reduce the equivalent fuel consumption effectively.

A Hybrid Electric Vehicle Dynamic Optimization Energy Management Strategy Based on a Compound-Structured Permanent-Magnet Motor

“…Afterwards, according to the required power, torque and speed of the engine, EM1 and EM2, the efficiency of the component is obtained by looking up the table. After this, the power loss, which satisfy the condition, is calculated by Equations (12) and (13). When the loss P loss is minimum, the corresponding (i CSPM , f 1 ) combination is obtained.…”

“…The CSPM has two mechanical ports and two electrical ports, which realize efficient energy transmission, so the CSPM has broad application prospects in wind power generation, ship propulsion systems and hybrid electric vehicles [1]. A correlative research study demonstrates that a suitable Energy Management Strategy (EMS) can reduce HEV fuel consumption [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], and it is also beneficial to emissions reduction. In [2], dynamic programming was investigated for reducing the fuel consumption of vehicles when the future driving conditions are acquired.…”

“…Moreover, this cannot improve the fuel economy significantly. To overcome this problem, [13,14] propose a rule-based strategy combine with a fuzzy inference system. As a fuzzy system, due to its simple logic, it can be implemented in real time applications.…”