Abstract
Energy control strategy is a key technology of hybrid electric vehicle, and its control effect directly affects the overall performance of the vehicle. The current control strategy has some shortcomings such as poor adaptability and poor real-time performance. Therefore, a transient energy control strategy based on terminal neural network is proposed. Firstly, based on the definition of instantaneous control strategy, the equivalent fuel consumption of power battery was calculated, and the objective function of the minimum instantaneous equivalent fuel consumption control strategy was established. Then, for solving the time-varying nonlinear equations used to control the torque output, a terminal recursive neural network calculation method using BARRIER functions is designed. The convergence characteristic is analyzed according to the activation function graph, and then the stability of the model is analyzed and the time efficiency of the error converging to zero is deduced. Using ADVISOR software, the hybrid power system model is simulated under two typical operating conditions. Simulation results show that the hybrid electric vehicle using the proposed instantaneous energy control strategy can not only ensure fuel economy but also shorten the control reaction time and effectively improve the real-time performance.
Highlights
Energy crisis and environmental pollution are two major problems that need to be solved in today’s social development
According to the optimization theory, the sum of minimum values is not equal to the minimum value of sum, so the instantaneous control strategy cannot achieve global optimization. e global optimal control strategy [16] can theoretically obtain the lowest fuel consumption in the real sense, but the outstanding disadvantage is that the driving condition of the vehicle must be known during the whole operation process, which is obviously inconsistent with the reality
The objective function of the instantaneous control strategy is defined, and the nonlinear equations used to control the torque output are solved by using terminal neural network models. en, an example is given based on the neural network model, and the simulation results are obtained. e simulation results show that the terminal network controller can achieve good fuel economy and improve the real-time control performance
Summary
Energy crisis and environmental pollution are two major problems that need to be solved in today’s social development. E global optimal control strategy [16] can theoretically obtain the lowest fuel consumption in the real sense, but the outstanding disadvantage is that the driving condition of the vehicle must be known during the whole operation process, which is obviously inconsistent with the reality. At present, this control strategy can only be used to evaluate other control effects, and it is difficult to be applied in real-time vehicle control
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