Energy Management of the Power-Split Hybrid Electric City Bus Based on the Stochastic Model Predictive Control

Yang, Rong; Yang, Xiaohu; Huang, Wei; Zhang, Song

doi:10.1109/access.2020.3047113

Cited by 16 publications

(6 citation statements)

References 43 publications

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“…SOC optimization or fuel reduction DDPG [5], DRL [40], DP [41], RL [42,43], DP, NN-based EMS [44], LTV-SMPC and PMP-stochastic MPC [45] Projected interior point method [3], LQP [45] DRL, rule-based, DDPG [40], Gaussian mixture model, SDP [41], QL, MPC [42], WF2SLOA [46], C/GMRES, BO [18], LQP, MPC, PMP [45] Hierarchical EMS [5], Hybrid EMS with torque split between the ICE and ESS [46], MPC EMS with non-linear losses [3] 16.34% of fuel savings [5], fuel economy improvement by 0.55% [40], LTV-SMPC and PMP-SMPC increase fuel economy by 8.79% and 14.42% respectively Prediction LSTM [5], Markov chain and LSTM [45] Power split with NN-based EMS [44] Speed [5,40,42] Prediction of mode and power split 2% higher compared to DP [44] Real-time power distribution MPC [5,42], C/GMRES [47] Polynomial fitting approx.…”

Section: Combination Of Algorithms Type Findingsmentioning

confidence: 99%

Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review

Recalde,

Cajo,

Velasquez

et al. 2024

Energies

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This paper provides a comprehensive review of machine learning strategies and optimization formulations employed in energy management systems (EMS) tailored for plug-in hybrid electric vehicles (PHEVs). EMS stands as a pivotal component facilitating optimized power distribution, predictive and adaptive control strategies, component health monitoring, and energy harvesting, thereby enabling the maximal exploitation of resources through optimal operation. Recent advancements have introduced innovative solutions such as Model Predictive Control (MPC), machine learning-based techniques, real-time optimization algorithms, hybrid optimization approaches, and the integration of fuzzy logic with neural networks, significantly enhancing the efficiency and performance of EMS. Additionally, multi-objective optimization, stochastic and robust optimization methods, and emerging quantum computing approaches are pushing the boundaries of EMS capabilities. Remarkable advancements have been made in data-driven modeling, decision-making, and real-time adjustments, propelling machine learning and optimization to the forefront of enhanced control systems for vehicular applications. However, despite these strides, there remain unexplored research avenues and challenges awaiting investigation. This review synthesizes existing knowledge, identifies gaps, and underscores the importance of continued inquiry to address unanswered research questions, thereby propelling the field toward further advancements in PHEV EMS design and implementation.

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Section: Combination Of Algorithms Type Findingsmentioning

confidence: 99%

Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review

Recalde,

Cajo,

Velasquez

et al. 2024

Energies

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“…The forget gate f t determines the information to be discarded and retained according to the unit state C t −1 at the previous moment, and the input x t determines the value to be updated through σ and tanh respectively and generates new candidate values for updating [ 27 ]. The value after the updating operation will be updated together with the Forget Gate f t , and the updated unit state C t is computed with the tanh function and Output Gate o t and then outputs h t [ 28 ]. The state update equation of LSTM basic unit are equations ( 8 )–( 13 ).…”

Section: Collision Risk Prediction Modelmentioning

confidence: 99%

“…h t −1 and h t are the outputs at corresponding moment. σ is the sigmoid activation function, and tanh is a hyperbolic tangent function [ 28 ]; W f , W i , W c , and W o and b f , b i , b c , and b o are the corresponding weight matrices and offsetting vectors.…”

Section: Collision Risk Prediction Modelmentioning

confidence: 99%

The Application of Automatic Identification System Information and PSO-LSTM Neural Network in CRI Prediction

Zhou

Xiao

et al. 2022

Computational Intelligence and Neuroscience

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Considering that collision accidents happen sometimes, it is necessary to predict the collision risk to ensure navigation safety. With the information construction in maritime and the popularity of automatic identification system application, it is more convenient to obtain ship navigation dynamics. How to obtain ship encounter dynamic parameters through automatic identification system information, assess ship collision risk, find out dangerous target ships, and give early warning and guarantee for ship navigation safety, is a problem that scholars have been studying. As an index to measure the degree of ship collision risk, CRI, namely, collision risk index, is usually obtained by calculating ship encounter parameters and comprehensive analysis. There are many factors that affect CRI, and the values of many parameters depend on expert judgment. The corresponding CRI has nonlinear and complex characteristics, which is highly correlated with the time sequence. In order to enhance the prediction accuracy and efficiency, PSO-LSTM neural network is applied in the paper to predict CRI. Experiments show that PSO-LSTM neural network can effectively predict collision risk and provide a reference for navigation safety.

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“…In [23] was developed a strategy based on model predictive controller for power split hybrid electric vehicles by developing two management schemes for power-split hybrid electric city bus (HECB), incorporating linear time-varying stochastic model predictive control and Pontriagin minimum principle stochastic model predictive control. Both strategies do have real time fast computational response at cost of complex calculations with increased efficiency.…”

Section: Introduction Advent Of High Pace Development Of Internal Com...mentioning

confidence: 99%

Efficiency enhancement strategy implementation in hybrid electric vehicles using sliding mode control

Ibrar

Ahmad

Safdar

et al. 2023

Electrical Engineering & Electromechanics

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Introduction. Hybrid electric vehicles are offering the most economically viable choices in today's automotive industry, providing best solutions for a very high fuel economy and low rate of emissions. The rapid progress and development of this industry has prompted progress of human beings from primitive level to a very high industrial society where mobility used to be a fundamental need. However, the use of large number of automobiles is causing serious damage to our environment and human life. At present most of the vehicles are relying on burning of hydrocarbons in order to achieve power of propulsion to drive wheels. Therefore, there is a need to employ clean and efficient vehicles like hybrid electric vehicles. Unfortunately, earlier control strategies of series hybrid electric vehicle fail to include load disturbances during the vehicle operation and some of the variations of the nonlinear parameters (e.g. stator’s leakage inductance, resistance of winding etc.). The novelty of the proposed work is based on designing and implementing two robust sliding mode controllers (SMCs) on series hybrid electric vehicle to improve efficiency in terms of both speed and torque respectively. The basic idea is to let the engine operate only when necessary keeping in view the state of charge of battery. Purpose. In proposed scheme, both performance of engine and generator is being controlled, one sliding mode controllers is controlling engine speed and the other one is controlling generator torque, and results are then compared using 1-SMC and 2-SMC’s. Method. The series hybrid electric vehicle powertrain considered in this work consists of a battery bank and an engine-generator set which is referred to as the auxiliary power unit, traction motor, and power electronic circuits to drive the generator and traction motor. The general strategy is based on the operation of the engine in its optimal efficiency region by considering the battery state of charge. Results .Mathematical models of engine and generator were taken into consideration in order to design sliding mode controllers both for engine speed and generator torque control. Vehicle was being tested on standard cycle. Results proved that, instead of using only one controller for engine speed, much better results are achieved by simultaneously using two sliding mode controllers, one controlling engine speed and other controlling generator torque.

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Energy Management of the Power-Split Hybrid Electric City Bus Based on the Stochastic Model Predictive Control

Cited by 16 publications

References 43 publications

Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review

Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review

The Application of Automatic Identification System Information and PSO-LSTM Neural Network in CRI Prediction

Efficiency enhancement strategy implementation in hybrid electric vehicles using sliding mode control

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