Abstract
For global optimal control strategy, it is not only necessary to know the driving cycle in advance but also difficult to implement online because of its large calculation volume. As an artificial intelligent-based control strategy, reinforcement learning (RL) is applied to an energy management strategy of a super-mild hybrid electric vehicle. According to time-speed datasets of sample driving cycles, a stochastic model of the driver’s power demand is developed. Based on the Markov decision process theory, a mathematical model of an RL-based energy management strategy is established, which assumes the minimum cumulative return expectation as its optimization objective. A policy iteration algorithm is adopted to obtain the optimum control policy that takes the vehicle speed, driver’s power demand, and state of charge (SOC) as the input and the engine power as the output. Using a MATLAB/Simulink platform, CYC_WVUCITY simulation model is established. The results show that, compared with dynamic programming, this method can not only adapt to random driving cycles and reduce fuel consumption of 2.4%, but also be implemented online because of its small calculation volume.
Highlights
With the increasing problems of global warming, air pollution, and energy shortage, hybrid electric vehicles (HEVs) have been extensively studied because of their potential to significantly improve fuel economy and reduce emissions.Energy management strategies are critical for HEVs to achieve optimal performance and energy efficiency through power split control
Based on a Ni-MH battery performance experiment, the electromotive force and internal resistance model are obtained as shown in Formulas (2) and (3): Esoc = E0 + ∑EiSOCi where E0 is the electromotive constant of the battery, Ei is the fitting coefficients, SOC is the state of charge, and Esoc is the electromotive force under the current state: Rsoc = δ0 (R0 + ∑λiSOCi)
A simulation is conducted on a MATLAB/ Simulink platform, taking ECE EUDC driving cycle as the simulation driving cycle and setting the initial SOC as 0.6
Summary
With the increasing problems of global warming, air pollution, and energy shortage, hybrid electric vehicles (HEVs) have been extensively studied because of their potential to significantly improve fuel economy and reduce emissions. Reference [1] proposed an energy management strategy based on a logic threshold and a fuzzy algorithm for improving fuel economy. Reference [5] proposed a stochastic model predictive control-based energy management strategy using the vehicle location, traveling direction, and terrain information of the area for HEVs operating in hilly regions with light traffic. A global optimal control strategy can guarantee an objective optimum design over a given driving cycle by distributing the power of the engine and motor. It can only be implemented offline because of the large volume of calculations involved. Reference [15] developed an online energy management controller for a plug-in HEV based on driving condition recognition and a genetic algorithm. Based on the MATLAB/Simulink simulation platform, a simulation is conducted on the economic performance of the vehicle
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