Pontryagin’s Minimum Principle-based power management of a dual-motor-driven electric bus

Zhang, Shuo; Xiong, Rui; Zhang, Chengning

doi:10.1016/j.apenergy.2015.08.129

Cited by 100 publications

(26 citation statements)

References 27 publications

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“…In contrast, the PMP requires fewer computations to achieve the global optimization. In the previous works, the DP algorithm was compared against the PMP, and it was confirmed that the latter can obtain almost the same control effect as the former in less time.…”

Section: Control Strategymentioning

confidence: 78%

“…It is necessary to formulate an appropriate control strategy to realize the potential of the PRHEV fully. The control strategies of hybrid electric vehicles are mainly divided into two types: rule‐based and optimization‐based . The rule‐based strategy relies on the experience of the engineers.…”

Section: Control Strategymentioning

confidence: 99%

“…However, the control strategy, which is based on the optimization algorithm, introduces the idea of optimization to discover optimal solutions for the systems. Typical algorithms include the equivalent fuel consumption algorithm (ECMS), dynamic programming (DP) algorithm, and the PMP . The ECMS considers minimizing the equivalent fuel consumption as the target to obtain an instantaneous optimal working point with a superior real‐time performance.…”

Section: Control Strategymentioning

confidence: 99%

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Performance evaluation and optimization of a novel plug‐in power‐reflux hybrid electric vehicle powertrain

Sun

Liu

et al. 2020

Int J Energy Res

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Summary In order to improve the fuel economy of hybrid electric vehicles, a novel plug‐in power‐reflux hybrid electric vehicle powertrain equipped with an electromechanical control continuously variable transmission (EMCVT) is proposed in this study. The proposed structure, which combines the dynamic property of the planetary mechanism and regulating characteristics of the continuous speed ratio with EMCVT, can transmit power in two modes, namely power‐reflux mode and pure‐CVT mode, thus realizing the multistage design of the overall transmission system. Hence, it demonstrates a wider speed ratio variation range and higher transmission efficiency than the “Pathfinder‐like” parallel configuration with traditional electro‐hydraulic CVT (EHCVT). To take full advantage of its potential for fuel economy, the highest transmission efficiency of the system is taken as the optimization objective for the target speed ratio of the system. The optimization results are incorporated in the control strategy based on Pontryagin's minimum principle to simulate fuel consumption under the new European driving cycle (NEDC), highway fuel economy test (HWFET), and urban dynamometer driving schedule (UDDS). Compared with the simulation of the “THS II‐like” configuration with similar vehicular parameters, the results show that the novel configuration has better fuel economy.

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Section: Control Strategymentioning

confidence: 78%

Section: Control Strategymentioning

confidence: 99%

Section: Control Strategymentioning

confidence: 99%

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Performance evaluation and optimization of a novel plug‐in power‐reflux hybrid electric vehicle powertrain

Sun

Liu

et al. 2020

Int J Energy Res

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“…A blended control strategy is developed based on the assumption that the entire driving cycle is known. The optimization algorithms, such as dynamic programming [15] and Pontryagin's Minimum Principle [16], are utilized to achieve global optimization in the blended strategy. Prof. Xiong in Ref.…”

Section: Introductionmentioning

confidence: 99%

Optimal Energy Management Strategy for a Plug-in Hybrid Electric Vehicle Based on Road Grade Information

Liu

et al. 2017

Energies

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Abstract:Energy management strategies (EMSs) are critical for the improvement of fuel economy of plug-in hybrid electric vehicles (PHEVs). However, conventional EMSs hardly consider the influence of uphill terrain on the fuel economy and battery life, leaving vehicles with insufficient battery power for continuous uphill terrains. Hence, in this study, an optimal control strategy for a PHEV based on the road grade information is proposed. The target state of charge (SOC) is estimated based on the road grade information as well as the predicted driving cycle on uphill road obtained from the GPS/GIS system. Furthermore, the trajectory of the SOC is preplanned to ensure sufficient electricity for the uphill terrain in the charge depleting (CD) and charge sustaining (CS) modes. The genetic algorithm is applied to optimize the parameters of the control strategy to maintain the SOC of battery in the CD mode. The pre-charge mode is designed to charge the battery in the CS mode from a reasonable distance before the uphill terrain. Finally, the simulation model of the powertrain system for the PHEV is established using MATLAB/Simulink platform. The results show that the proposed control strategy based on road-grade information helps successfully achieve better fuel economy and longer battery life.

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“…Since there exists some uncertainty for driving cycles, driver's habits, and weather conditions that can influence the energy distribution in the PHEV, from this point, it can be said that the energy management is a stochastic optimization problem. Actually, popular control candidates can be divided into four types: (1) rule based control method [3][4][5]; (2) intelligent control methods, including artificial neural network (ANN) [6,7], fuzzy logic [8,9], model predictive control (MPC) [10,11], and machine learning algorithm [12,13]; (3) analytic methods [14,15]; and (4) optimization based control method, including deterministic dynamic programming (DP) [1,[16][17][18][19]], Pontryagin's Minimum Principle (PMP) [20,21], quadratic programming (QP) [22,23], and convex optimization [24][25][26]. These methods' purpose can include improving the fuel economy, reducing emissions [27,28], prolonging cycling life of the battery pack [2,29], minimizing the operation cost [30], etc.…”

Section: Introductionmentioning

confidence: 99%

Comparisons of Energy Management Methods for a Parallel Plug-In Hybrid Electric Vehicle between the Convex Optimization and Dynamic Programming

et al. 2018

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This paper proposes a comparison study of energy management methods for a parallel plug-in hybrid electric vehicle (PHEV). Based on detailed analysis of the vehicle driveline, quadratic convex functions are presented to describe the nonlinear relationship between engine fuel-rate and battery charging power at different vehicle speed and driveline power demand. The engine-on power threshold is estimated by the simulated annealing (SA) algorithm, and the battery power command is achieved by convex optimization with target of improving fuel economy, compared with the dynamic programming (DP) based method and the charging depleting-charging sustaining (CD/CS) method. In addition, the proposed control methods are discussed at different initial battery state of charge (SOC) values to extend the application. Simulation results validate that the proposed strategy based on convex optimization can save the fuel consumption and reduce the computation burden obviously.

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Pontryagin’s Minimum Principle-based power management of a dual-motor-driven electric bus

Cited by 100 publications

References 27 publications

Performance evaluation and optimization of a novel plug‐in power‐reflux hybrid electric vehicle powertrain

Performance evaluation and optimization of a novel plug‐in power‐reflux hybrid electric vehicle powertrain

Optimal Energy Management Strategy for a Plug-in Hybrid Electric Vehicle Based on Road Grade Information

Comparisons of Energy Management Methods for a Parallel Plug-In Hybrid Electric Vehicle between the Convex Optimization and Dynamic Programming

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