Heuristic Dynamic Programming Based Online Energy Management Strategy for Plug-In Hybrid Electric Vehicles

Liu, Jichao; Chen, Yangzhou; Zhan, Jingyuan; Shang, Fei

doi:10.1109/tvt.2019.2903119

Cited by 80 publications

(45 citation statements)

References 26 publications

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“…is a nonlinear function representing the architecture of the approximator, Φ d (s) is the vector of feature or basis functions of the states, and ψ is the parameter vector as shown in Fig.6. For EMS application, ANN can represent the iEMS controller, velocity predictor [87], model of vehicle [88], and driving trends predictor. Quality of predictive EMSs highly depends on the accuracy of the predicted variables.…”

Section: Data-driven Intelligent Emssmentioning

confidence: 99%

Intelligent Energy Management Systems for Electrified Vehicles: Current Status, Challenges, and Emerging Trends

Ostadian

Ramoul

Biswas

et al. 2020

IEEE Open J. Veh. Technol.

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By increasing the electrified vehicles trend, it is crucial to have a survey of what is done before and what is the open challenge for improving this industry. Powertrain electrification has heightened the need for an energy management strategy, which has been a continuing concern in the development of electrified vehicles. An energy management control unit manages power flow between different energy sources in an electrified powertrain that directly affects vehicle performance. There are inadequate numbers of studies on the most recent energy management strategies, which are compatible with real-world scenarios. Intelligent energy management strategies requirements are discussed in detail, and it is categorized to principle-based, data-driven, and composite-based methods. Besides, enabling technologies for implementing an energy management system with a comparison of different controller chips are described to give readers an experimental view. Future trends and existing challenges are presented, which generate fresh insight into energy management strategies. Index Terms-data-driven methods, electric vehicles, intelligent energy management strategy, reinforcement learning, powertrain architecture.

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Section: Data-driven Intelligent Emssmentioning

confidence: 99%

Intelligent Energy Management Systems for Electrified Vehicles: Current Status, Challenges, and Emerging Trends

Ostadian

Ramoul

Biswas

et al. 2020

IEEE Open J. Veh. Technol.

View full text Add to dashboard Cite

show abstract

“…12,13 Limited to experiences of rule makers and complex variables of rules formulating, the potential of energy saving and emission reduction for PHEVs is hard to be further improved. Gradually, numerous optimizationbased and evaluation-based energy management strategies have been conducted to effectively improve the fuel economy and effciency, [14][15][16] including sequential quadratic programming, 17 model predictive control (MPC), 18,19 dynamic programming (DP), 7,20,21 pontryagin's minimum principle (PMP), 1, [22][23][24][25] equivalent consumption minimization strategy (ECMS), 26 nonlinear programming, 27 and game theory. 28 Actually, ECMS can yield the optimal fuel economy similar to DP on the premise of known full driving cycle in advance.…”

Section: Introductionmentioning

confidence: 99%

A novel real‐time energy management strategy for plug‐in hybrid electric vehicles based on equivalence factor dynamic optimization method

2020

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The universal adaptive equivalent consumption minimization strategy (A-ECMS) has the potential of being implemented in real-time for plug-in hybrid electric vehicles (PHEVs). However, the imprecise prediction of a longterm future driving cycle and biggish computation burdens remain the barriers for further real vehicle application. Thus, it is of great significance to develop a real-time optimal energy management strategy for PHEVs by weakening the influence of future driving cycle to the control accuracy and improving its computation efficiency. In this paper, a novel real-time energy management strategy for PHEVs based on equivalence factor (EF) dynamic optimization method is proposed. Firstly, a novel proportional plus integral adaption law for calculating the dynamic optimal EF is established for A-ECMS using only instantaneous information of current vehicle speed and battery state of charge. Second, three key coefficients are obtained and converted into a threedimensional look up tables, so as to determine the dynamic optimal EF. Finally, the method of fast searching the optimal engine torque is proposed, which significantly enhances the computational efficiency. Compared with A-ECMS, the computational time of A-ECMS2 is decreased near 94.8% and the deviation of fuel consumption is controlled within 4.4%. Both the numerical results and hardware-in-loop results prove that the proposed novel energy management strategy A-ECMS2 has better real-time performance and less computing burden than the general A-ECMS.

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“…Parameter optimization of PHEVs is related to the energy management strategy, and energy management strategies need to be developed before parameter optimization. At present, the research on the energy management strategy for PHEVs mainly focuses on the development of the advanced optimization algorithm, such as the algorithm based on the minimum equivalent fuel consumption [26][27][28][29], the dynamic programming algorithm [30][31][32], stochastic dynamic programming [33], the algorithm based on convex optimization [2,34] and the model predictive control algorithm [35][36][37][38]. Although the above-mentioned optimization algorithm can obtain the local or global optimum, it is difficult to apply to real vehicle control for hardly knowing the driving cycles beforehand or the large amount of calculation.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization for Plug-In 4WD Hybrid Electric Vehicle Powertrain

et al. 2019

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This paper focuses on the parameter optimization for the CVT (a continuously variable transmission) based plug-in 4WD (4-wheel drive) hybrid electric vehicle powertrain. First, the plug-in 4WD hybrid electric vehicle (plug-in 4WD HEV)’s energy management strategy based on the CD (charge depleting) and CS (charge sustain) mode is developed. Then, the multi-objective optimization’s mathematical model, which aims at minimizing the electric energy consumption under the CD stage, the fuel consumption under the CS stage and the acceleration time from 0–120 km/h, is established. Finally, the multi-objective parameter optimization problem is solved using an evolutionary based non-dominated sorting genetic algorithms-II (NSGA-II) approach. Some of the results are compared with the original scheme and the classical weight approach. Compared with the original scheme, the best compromise solution (i.e., electric energy consumption, fuel consumption and acceleration time) obtained using the NSGA-II approach are reduced by 1.21%, 6.18% and 5.49%, respectively. Compared with the weight approach, the Pareto optimal solutions obtained using NSGA-II approach are better distributed over the entire Pareto optimal front, as well as the best compromise solution is also better.

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Heuristic Dynamic Programming Based Online Energy Management Strategy for Plug-In Hybrid Electric Vehicles

Cited by 80 publications

References 26 publications

Intelligent Energy Management Systems for Electrified Vehicles: Current Status, Challenges, and Emerging Trends

Intelligent Energy Management Systems for Electrified Vehicles: Current Status, Challenges, and Emerging Trends

A novel real‐time energy management strategy for plug‐in hybrid electric vehicles based on equivalence factor dynamic optimization method

Multi-Objective Optimization for Plug-In 4WD Hybrid Electric Vehicle Powertrain

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