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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

With the development of autonomous vehicles, research on energy-efficient eco-driving is becoming increasingly important. The optimal control problem of determining the speed profile of the vehicle for minimizing energy consumption is a challenging problem that necessitates the consideration of various aspects, such as the vehicle energy consumption, slope of the road, and driving environment, e.g., the traffic and other vehicles on the road. In this study, an approach using reinforcement learni

The energy management strategy of a hybrid electric vehicle directly determines the fuel economy of the vehicle. As a supervisory control strategy to divide the required power into its multiple power sources, engines and batteries, many studies have been conducting using rule-based and optimization-based approaches for energy management strategy so far. Recently, studies using various machine learning techniques have been conducted. In this paper, a novel control framework implementing Model-bas

Fuel cell electric vehicles use fuel cells as their main power source; the vehicle is driven by an electric motor, and have an electric battery as a secondary power source that stores regenerative braking energy and assists driving. To reduce the hydrogen fuel consumption by using these fuel cells and electric batteries efficiently, an energy management strategy is needed for the proper distribution of power among them. In this study, model-based reinforcement learning was utilized for energy ma

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 sho

For hybrid electric vehicle, it is necessary to control power distribution among multiple power sources to improve fuel economy performance of vehicle. In this paper, power management strategy of hybrid electric vehicle using Dynamic programming is studied. Deterministic dynamic programming could present outstanding fuel economy, while its application as real time control of vehicle is limited. Thus, different kinds of power management strategy using dynamic programming are studied. Stochastic d

<div class="section abstract"><div class="htmlview paragraph">This paper is concerned with the energy management strategy of hybrid electric vehicle using stochastic dynamic programming. The aim is the control strategy of the power distribution for hybrid electric vehicle powertrains to minimize fuel consumption while maintaining drivability. The fuel economy of hybrid electric vehicle is strongly influenced by power management control strategy. Rule-based control strategy is popular

This paper is concerned with power management strategy of hybrid electric vehicle. Dynamic programming based power split ratio line control strategy is studied for parallel type hybrid electric vehicle to improve fuel economy performance. Dynamic programming, one of the optimization based control strategy, is powerful tool to provide optimal solution for hybrid electric vehicle's power management problem. However, it is considered not implementable as real time control strategy due to its non-ca

This paper introduces a method for component sizing using optimization algorithm. Sizing vehicle's powertrain component is a complicated problem, resulting in vehicle's fuel economy and dynamic performance directly. Especially, as powertrains of modern vehicles become more complicated, it takes a lot of efforts to design each component size of the vehicle. In this study, we present a component sizing process using optimization algorithm. The process is developed based on the forward-looking vehi

This paper proposes to use hybrid model predictive control (HMPC) for energy management in hybrid electric vehicle (HEV) using an efficient formulation. HEV has two sources of energy - electric motor and internal combustion engine (ICE) - allowing it an additional degree of freedom to optimize the ratio between the use of two energy sources. HEV energy management is crucial to exploit its potential to reduce fuel consumption and emissions. However, it is challenging to achieve the optimal soluti

In this paper, component sizing and analysis of the novel plug-in hybrid electric vehicle powertrain configuration is conducted. Newly proposed powertrain configuration in prior study has an internal combustion engine and two electric motors. To optimize component size of the vehicle system and reduction gear ratio, component sizing methodology is proposed and conducted. Required power for vehicle's dynamic performance is calculated to decide minimum power requirement of powertrain component com