Analysis Of Energy Management Strategies Research Articles

Comparative Analysis of Energy Management Strategies for HEV: Dynamic Programming and Reinforcement Learning

Energy management strategy is an important factor in determining the fuel economy of hybrid electric vehicles; thus, much research on how to distribute the required power to engines and motors of hybrid vehicles is required. Recently, various studies have been conducted based on reinforcement learning to optimally control the hybrid electric vehicle. In fact, the fundamental control approach of reinforcement learning shares many control frameworks with the control approach by using deterministic dynamic programming or stochastic dynamic programming. In this study, we compare the reinforcement learning based strategy by using these dynamic programming-based control approaches. For optimal control of hybrid electric vehicle, each control method was compared in terms of fuel efficiency by performing simulation by using various driving cycles. Based on our simulations, we showed the reinforcement learning-based strategy can obtain global optimality in the optimal control problem with an infinite horizon, which can also be obtained by stochastic dynamic programming. We also showed that the reinforcement learning-based strategy can present a solution close to the optimal one using deterministic dynamic programming, while a reinforcement learning-based strategy is more appropriate for a time variant controller with boundary value constraints. In addition, we verified the convergence characteristics of the control strategy based on reinforcement learning, when transfer learning was performed through value initialization using stochastic dynamic programming.

Analysis of energy management strategies for hybrid electric vehicles in urban driving conditions

The pursuit of fuel consumption reduction by vehicles leads to a sudden increase in the share of hybrid and electric drives in the vehicle market. Replacing hybrid vehicles with electric vehicles requires long-term technological solutions, both for the infrastructure and the vehicles themselves. Therefore, one of the leading types of passenger car drives is currently the hybrid drive. The generated work share of electric drives used to power hybrid vehicles is a determinant of the viability of using electric drives. The article estimates the operating time share of electric and hybrid modes operation in real driving conditions (RDC) based on the latest Toyota hybrid model. The research object was a vehicle from the crossover group equipped with a fourth generation hybrid drive. Analysis of the drives operation allowed to determine the conditions of energy flow and determine the work share of the electric drive in the total driving time.

A Comparative Analysis of Energy Management Strategies for Hybrid Electric Vehicles

This paper presents a formalization of the energy management problem in hybrid electric vehicles and a comparison of three known methods for solving the resulting optimization problem. Dynamic programming (DP), Pontryagin’s minimum principle (PMP), and equivalent consumption minimization strategy (ECMS) are described and analyzed, showing formally their substantial equivalence. Simulation results are also provided to demonstrate the application of the strategies. The theoretical background for each strategy is described in detail using the same formal framework. Of the three strategies, ECMS is the only implementable in real time; the equivalence with PMP and DP justifies its use as an optimal strategy and allows to tune it more effectively.