Adaptive energy control strategy of PHEV based on the Pontryagin’s minimum principle algorithm

Shi, Dapai; Liu, Kangjie; Wang, Yun; Liu, Ruijun; Li, Shicheng; Sun, Yi

doi:10.1177/16878140211035598

Cited by 6 publications

(5 citation statements)

References 31 publications

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“…This method is computationally small but can obtain approximate global optimum results. In recent years, PMP has been widely used in hybrid energy storage system, plug-in hybrid electric vehicle, and microgrid (Bai et al, 2019;Shi et al, 2021). Farhadinia (2014) established the necessary optimality conditions for the fuzzy optimization problems and the fuzzy PMP for the fuzzy optimal control problems using the concepts of differentiability and integrability of a fuzzy mapping, parameterized by the left-and right-hand functions of its alevel sets, together with the concept of fuzzy variation.…”

Section: Background and Motivationsmentioning

confidence: 99%

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Adaptive global energy optimal management strategy based on the Pontryagin’s minimum principle: An isolated microgrid example

Wang

Hou

Chen

et al. 2023

Transactions of the Institute of Measurement and Control

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Optimal control is a common control strategy to solve complex non-linear systems. Pontryagin’s minimum principle, as a typical optimal control theory, is widely used in energy management systems. Due to the randomness of new energy generation and the diversity of load power consumption, microgrid energy management system is a complex non-linear stochastic system. The efficient operation of microgrid energy management strategy largely depends on the real-time performance of the control algorithm. Pontryagin’s minimum principle is an offline control strategy, whose co-state variable λ depends on expert experience and needs to be manually adjusted according to different working conditions, so it is difficult to achieve real-time optimal control. First, the energy management system model of island-type microgrid is established in this paper. In order to improve the economy of power generation and prevent battery over-charge or over-discharge, the state of charge (SOC) change of battery pack is comprehensively considered, and the fuel cost and pollutant treatment cost are taken as the objective function. Second, an adaptive Pontryagin’s minimum principle energy management strategy is proposed. By introducing the penalty mechanism and adjusting the parameters adaptively, the problem that the open-loop control of Pontryagin’s minimum principle needs to adjust the co-state variables in real time is solved. Comparative experiments show that compared with Pontryagin’s minimum principle and particle swarm optimization, adaptive Pontryagin’s minimum principle can not only limit battery SOC within the ideal range but also effectively reduce the daily operation cost of microgrid. Experimental results verify the effectiveness of the proposed algorithm.

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Section: Background and Motivationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive global energy optimal management strategy based on the Pontryagin’s minimum principle: An isolated microgrid example

Wang

Hou

Chen

et al. 2023

Transactions of the Institute of Measurement and Control

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show abstract

“…where SOC min is the SOC expectation at the end of the stroke, set to 0.30, SOC max is the initial value of the battery SOC, set to 0.70, P bat is the battery power, T e is the torque of the engine, and T m is the torque of the motor. According to the description of PMP, the optimal control sequence u * minimizes the Hamilton function value and minimizes the optimization objective function [21]. After the cooperative state variable λ…”

Section: Energy Management Optimization Problemmentioning

confidence: 99%

“…Assuming f (x) as the objective function to be minimized, the current best position of individual m can be determined by Equation (20). The fitness function f of PSO is expressed as Equation (21).…”

Section: Driving Cycle Prediction Based On Pso−bp Neural Networkmentioning

confidence: 99%

Adaptive Energy Management Strategy Based on Intelligent Prediction of Driving Cycle for Plug−In Hybrid Electric Vehicle

Shi

Liu

et al. 2022

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Under the dual−carbon goal, the research on energy conservation and emission reduction of new energy vehicles has once again become a current hotspot, and plug−in hybrid electric vehicles (PHEVs) are the first to bear the brunt. In order to improve the fuel economy of PHEV, an adaptive energy management strategy is designed on the basis of the intelligent prediction of driving cycles. Firstly, according to the vehicle dynamics model, the optimal control objective function of PHEV is established, and the relationship between vehicle fuel consumption and driving cycle is analyzed. Secondly, the initial weights and threshold of the backpropagation (BP) neural network are optimized using the particle swarm optimization (PSO) algorithm, and a PSO−BP neural network vehicle velocity prediction controller is established. Thirdly, combined with the approximate equivalent consumption minimization strategy (ECMS) algorithm to calculate the optimal initial equivalent factor in the prediction time domain, the fast−planning SOC and PI control are introduced to determine the optimal equivalent factor sequence, and the optimal torque distribution ratio of the engine and motor is calculated. Lastly, three different energy management strategies are simulated and verified under six China light−duty vehicle test cycle−passenger car (6*CLTC−P) driving cycles. Simulation results show that the established velocity prediction model has good prediction accuracy, and the proposed adaptive energy management strategy based on prediction is 9.85% higher than the rule−based strategy in terms of fuel saving rate and 5.30% higher than the ECMS strategy without prediction, which further improves the fuel saving potential of PHEV.

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“…With an increasing number of vehicles, how to save energy and reduce emissions have become the research objectives of many scholars. Guangdong Tian analyzed the reuse of abandoned vehicles in China and put forward a series of methods to improve processing efficiency [17][18][19]. In addition, energy vehicles using clean energy are also research hotspots.…”

Section: Introductionmentioning

confidence: 99%

Optimal Control Strategy of Path Tracking and Braking Energy Recovery for New Energy Vehicles

et al. 2022

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In order to further improve the stability of path tracking control and fuel economy of new energy vehicles, an optimal control strategy of path tracking and braking energy recovery is proposed. First, a model predictive controller is designed based on the three-degrees of freedom dynamics model of the vehicle according to the idea of hierarchical control, and a fuzzy yaw torque controller is established with the desired yaw velocity and side slip angle of the mass center as constraints. Second, at high-speed driving conditions, the executive layer of the component distributes the braking torque according to the braking energy recovery control strategy. Finally, the optimal control strategy of path tracking and braking energy recovery is verified by Carsim/Advisor/Simulink software under different driving speeds. The results show that the optimized control strategy can improve the tracking accuracy and driving stability of a vehicle with large curvature turning and further improve the fuel economy of new energy vehicles under the premise of meeting the control requirements.

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Adaptive energy control strategy of PHEV based on the Pontryagin’s minimum principle algorithm

Cited by 6 publications

References 31 publications

Adaptive global energy optimal management strategy based on the Pontryagin’s minimum principle: An isolated microgrid example

Adaptive global energy optimal management strategy based on the Pontryagin’s minimum principle: An isolated microgrid example

Adaptive Energy Management Strategy Based on Intelligent Prediction of Driving Cycle for Plug−In Hybrid Electric Vehicle

Optimal Control Strategy of Path Tracking and Braking Energy Recovery for New Energy Vehicles

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