Fast dynamic-programming algorithm for solving global optimization problems of hybrid electric vehicles

Chen, Shuang; Hu, Minghui; Guo, Shanqi

doi:10.1016/j.energy.2023.127207

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…Among the forms of optimization, several studies have been conducted using DP (73)(74)(75)(76)(77) . Brahma et al (73) defined the problem as the determination of the power-generation profile that minimizes the overall energy consumption for the Federal Urban Driving Schedule cycle in a series hybrid vehicle, and verified that the overall SOC constraint can be considered by applying adjustable weights with respect to the amount of stored electrical energy consumed/restored in a timestep in the evaluation function.…”

Section: Control Of the Advanced Powertrain System And V2x Technologiesmentioning

confidence: 99%

Control Technologies for Advanced Engines and Powertrains: A Review

Yamasaki,

Kim

2024

IJAE

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Control technology for engines and/or powertrains is becoming increasingly important for achieving higher energy efficiency and lower CO2 emissions in the real world in terms of the carbon neutrality of automobiles. CO2 emissions can be reduced using advanced combustion technologies and/or powertrain systems including hybrid powertrains. Such advanced combustion technologies show low robustness to changes in operation conditions, and advanced powertrain systems are difficult to optimize for complicated systems and uncertainty in the real road driving. Therefore, control technology is essential for taking full advantage of such hardware capabilities. Furthermore, the effective use of various information that is currently available, such as that on traffic signals, maps, and other cars, could lead to further reductions in CO2 emissions and energy consumption. This review summarizes the research on control technology, especially for advanced combustion and powertrain systems, and discusses the role of powertrain control and its future prospects.

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Section: Control Of the Advanced Powertrain System And V2x Technologiesmentioning

confidence: 99%

Control Technologies for Advanced Engines and Powertrains: A Review

Yamasaki,

Kim

2024

IJAE

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“…Since it is a discrete system, we used k instead of t to represent the current control interval. Based on Equation (35), we applied Successive Quadratic Programming (SQP) based on the Newton-Raphson method to determine the solution of nonlinear constrained cost function minimization [32].…”

Section: Empcmentioning

confidence: 99%

“…As shown in the analysis in [31,35], DP is used for a global optimization algorithm. However, its real-time application is hindered by its high consumption of computational resources and time.…”

Section: Dpmentioning

confidence: 99%

Optimal EMS Design for a 4-MW-Class Hydrogen Tugboat: A Comparative Analysis Using DP-Based Performance Evaluation

Hwang,

Lee,

Ryu

et al. 2024

Energies

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In the current trend of hydrogen fuel cell-powered ships, batteries are used together with fuel cells to overcome the limitations of fuel cell technology. However, performance differences arise depending on fuel cell and battery configurations, load profiles, and energy management system (EMS) algorithms. We designed four hybrid controllers to optimize EMS algorithms for achieving maximum performance based on target profiles and hardware. The selected EMS is based on a State Machine, an Equivalent Consumption Minimization Strategy (ECMS), Economic Model Predictive Control (EMPC), and Dynamic Programming (DP). We used DP to evaluate the optimal design state and fuel efficiency of each controller. To evaluate controller performance, we obtained a 4-MW-class tug load profile as a reference and performed simulations based on Nedstack’s fuel cells and a lithium-ion battery model. The constraints were set according to the description of each equipment manual, and the optimal controller was derived based on the amount of hydrogen consumed by each EMS under the condition of completely tracking the load profile. As a result of simulating the hybrid fuel cell–battery system by applying the load profile of the tugboat, we found that the 4-MW EMPC, which requires more state variables and control inputs, is the most fuel-efficient controller.

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“…Moreover, a range of optimal control theories have been applied, encompassing dynamic programming (DP), Pontryagin's minimum principle (PMP), and model predictive control (MPC), among other methodologies. The DP algorithm, which hinges on the Bellman equation [13], has been extensively explored with different DP-based methods [14,15], such as forward-looking DP [16], to seek optimal solutions. Nevertheless, this method is computationally demanding and struggles with real-time adaptability in dynamic driving environments.…”

Section: Introductionmentioning

confidence: 99%

Energy-Saving Speed Planning for Electric Vehicles Based on RHRL in Car following Scenarios

Xu,

Zhang,

et al. 2023

Sustainability

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Eco-driving is a driving vehicle strategy aimed at minimizing energy consumption; that is, it is a method to improve vehicle efficiency by optimizing driving behavior without making any hardware changes, especially for autonomous vehicles. To enhance energy efficiency across various driving scenarios, including road slopes, car following scenarios, and traffic signal interactions, this research introduces an energy-conserving speed planning approach for self-driving electric vehicles employing reinforcement learning. This strategy leverages vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication to acquire real-time data regarding traffic signal timing, leading vehicle speeds, and other pertinent driving conditions. In the framework of rolling horizon reinforcement learning (RHRL), predictions are made in each window using a rolling time domain approach. In the evaluation stage, Q-learning is used to obtain the optimal evaluation value, so that the vehicle can reach a reasonable speed. In conclusion, the algorithm’s efficacy is confirmed through vehicle simulation, with the results demonstrating that reinforcement learning adeptly modulates vehicle speed to minimize energy consumption, all while taking into account factors like road grade and maintaining a secure following distance from the preceding vehicle. Compared with the results of traditional adaptive cruise control (ACC), the algorithm can save 11.66% and 30.67% of energy under two working conditions.

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Fast dynamic-programming algorithm for solving global optimization problems of hybrid electric vehicles

Cited by 8 publications

References 26 publications

Control Technologies for Advanced Engines and Powertrains: A Review

Control Technologies for Advanced Engines and Powertrains: A Review

Optimal EMS Design for a 4-MW-Class Hydrogen Tugboat: A Comparative Analysis Using DP-Based Performance Evaluation

Energy-Saving Speed Planning for Electric Vehicles Based on RHRL in Car following Scenarios

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