Abstract:Aiming at the problem of energy inconsistency during the charging and discharging process of lithium battery packs, this paper proposes a two-level equilibrium topology, which is divided into inner and outer groups. The inductance-based ring structure equalization circuit is used in the battery pack, which realizes a new type of active equalization of bidirectional ring transfer of energy between adjacent single cells and the head and tail cells. A centralized balance topology based on a single inductor is used outside the battery pack, which can achieve balance between any battery packs between the packs. In terms of balancing control strategy, taking the battery state of charge as the balancing variable, a fuzzy logic control algorithm is designed to dynamically adjust the balancing current to reduce the balancing time and improve the balancing efficiency. Using MATLAB/Simulink software to build and simulate the model, the experimental results show that the energy transfer topology proposed in this paper reduces the equalization time by 24.46% compared with the traditional Buck-Boost circuit energy transfer topology between adjacent cells. In addition, compared with the fuzzy logic control algorithm using the fuzzy logic control algorithm under static and charge-discharge conditions, the standard deviation of single cells after equalization decreased by about 11%. The feasibility of the equalization scheme is verified.

Abstract:Aiming at the problems of long equalization time and large energy loss in the equalization process of series-connected lithium-ion battery packs, a two-level equalization topology is designed in this paper, and a variable universe fuzzy Logic control strategy based on battery state of charge is designed for this topology. The proposed topology adopts an improved Buck-Boost circuit within the battery pack to optimize the equalization path, and a centralized single-inductance equalization circuit is used between the battery packs, which can achieve equalization between any battery packs. The proposed strategy introduces a contraction-expansion factor on the basis of fuzzy logic control to flexibly regulate the input domain, which further improves the equalization speed and energy utilization by precisely adjusting the equalization current. Finally, the equalization system is built for validation, and the results show that the topology of this paper reduces the equalization time by about 12.53% compared to the grouped Buck-Boost topology. Under the same static and charging/discharging conditions, the strategy in this paper not only reduces the equalization time by about 20.98% but also improves the energy utilization by about 7% compared with the FLC algorithm. The feasibility of the equalization scheme in this paper is verified.

Abstract:A multi-objective optimization method for energy management strategy (EMS) of the fuel cell hybrid electric vehicle (FCHEV) is proposed to improve the efficiency of the drive system and optimize the durability of fuel cell. The equivalent hydrogen consumption model of the hybrid power system is established according to the power flow and the efficiency characteristics of key components. In addition, the lifetime degradation of fuel cell based on the load variation is considered. The energy management system is achieved by presenting an intelligent power allocation method, that is, the control strategy based on fuzzy logic control (FLC). In further research, in order to ameliorate the energy management strategy, the parameters of the fuzzy controller are optimized with the assistance of genetic algorithm (GA). A multi-objective optimization problem which takes equivalent fuel consumption and fuel cell lifetime as optimization targets is proposed. The improved fast non-dominated sorting genetic algorithm (NSGA-II) is used to solve the multi-objective optimization problem, so as to optimize the control parameters. Finally, the optimization results of the above algorithm are tested, and the optimized strategy and other strategies are simulated by the advanced vehicle simulator (ADVISOR) under typical conditions. The results demonstrate that the optimization is effective, and the optimized control strategy has a considerable degree of superiority.