Abstract
Hybrid electric vehicles, operated by engines and motors, require an energy management strategy to achieve competitive fuel economy performance. The equivalent consumption minimization strategy is a well-known algorithm that can be employed for the energy management of hybrid electric vehicles, based on the concept of the equivalent cost of fossil fuels and electric battery energy. However, in the equivalent consumption minimization strategy approach, a parameter called the equivalent factor should be determined to obtain the optimal control policy. In this study, reinforcement learning based approaches are proposed to determine the equivalent factor. First, we show that the equivalent factor can be indirectly extracted from the reinforcement learning results, using the control action from reinforcement learning for the specific driving cycle. In addition, a novel approach that combines reinforcement learning and the equivalent consumption minimization strategy is proposed, where the equivalent factor is determined based on the interaction between the reinforcement learning agent and driving environment, while the control input is decided by the equivalent consumption minimization strategy based on the determined equivalent factor. A model-based reinforcement learning method is used, and the proposed method is validated for vehicle simulation using a parallel hybrid electric vehicle. The simulation results show that the proposed method can achieve a near-optimal solution, which is close to the global solution obtained with the dynamic programming approach (96.7% compared to dynamic programming result in average), and improved performance of 4.3% in average compared with the existing adaptive equivalent consumption minimization strategy.
Highlights
Hybrid electric vehicles (HEVs) require energy management strategies to optimally distribute the required demand power to their multiple power sources, including internal combustion engines and electric batteries
The basic idea of the equivalent consumption minimization strategy (ECMS) is to evaluate the instantaneous cost J at time k, consisting of the fuel consumption mand battery state of charge (SOC) usage SOC to find control input u, which can be represented by the equivalent factor λ as follows: Jk min(m (u)
CONTRIBUTIONS in this study, we developed an energy management strategy based on a reinforcement learning (RL) approach combined with the conventional ECMS
Summary
Hybrid electric vehicles (HEVs) require energy management strategies to optimally distribute the required demand power to their multiple power sources, including internal combustion engines and electric batteries. MOTIVATION AND ECMS BASED METHODS these energy management strategies have disadvantages in that the equivalent factor value or co-state value should be known, which is dependent on the future driving cycle. This is called an a priori problem in which to find the optimal solution, future driving cycle information should be known in advance. The equivalent factor can be estimated by evaluating the vehicle speed and corresponding energy consumption as in reported [4], or the co-state value can be determined based on the shooting method for a given driving cycle [5], wherein the boundary value problem is converted into an initial value problem and the final state value can be obtained using a differential equation. Even in the case of these adaptive methods, the problem of determining the initial equivalent factor remains to be solved
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