Two-step optimal energy management strategy for single-shaft series-parallel powertrain

Li, Liang; Yan, Bingjie; Song, Jian; Zhang, Yuanbo; Jiang, Guirong; Li, Lei

doi:10.1016/j.mechatronics.2016.03.006

Cited by 21 publications

(9 citation statements)

References 25 publications

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“…To make deterministic rule-based strategies more effective, mode-switching thresholds can be optimized using several intelligent algorithms, such as the genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), quadratic programming (QP), and so forth. For instance, mode-switching thresholds were optimized by simulated annealing-particle swarm optimization (SA-PSO) in [26] to obtain the ideal mode-switching sequence. In [37], a hybrid algorithm combining GA with SA was applied to simultaneously optimize powertrain and control parameters, resulting in a better convergence speed and offering a global searching ability to obtain the best comprehensive performance for a plug-in hybrid electric bus (PHEB).…”

Section: Deterministic Rule-based Strategymentioning

confidence: 99%

“…By considering the statistics of traffic speed profiles, the SDP method based on a modified policy iteration can be adopted to generate a time-invariant state-dependent power split strategy to optimize fuel consumption and charge sustenance of HEVs in a general sense [90]. In [26], a pre-optimization based on the basic operating modes was carried out to obtain the optimal decisions in advance, followed by obtaining the optimal combination of the operating modes and the power split between engine and motor based on SDP. In the SDP algorithm, a homogenous Markov chain in a finite horizon is used to model the driver demand, whose stochastic transition probability matrix is constructed from 15 driving cycles.…”

Section: Stochastic Dynamic Programming (Sdp)mentioning

confidence: 99%

“…Therefore, it is necessary to consider the impact of different penalty factors on the performance of the optimal control strategy, which is mainly reflected in the SOC domain, the deviation from initial SOC, and the fuel economy. According to the effects of difficult penalty factors on the optimal results, the most suitable value was chosen as 8 to balance fuel economy and battery performance in [26]. Higher values of λ tend to consider the energy balance costs to make the vehicle operate in a more charge sustaining fashion; however, this requires more iterations to ensure the convergence of the solution and more computational times.…”

Section: Stochastic Dynamic Programming (Sdp)mentioning

confidence: 99%

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Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies

Kong

Liang

et al. 2019

Applied Sciences

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This paper presents a comprehensive review of energy management control strategies utilized in hybrid electric vehicles (HEVs). These can be categorized as rule-based strategies and optimization-based strategies. Rule-based strategies, as the most basic strategy, are widely used due to their simplicity and practical application. The focus of rule-based strategies is to determine and optimize the optimal threshold for mode switching; however, they fall into a local optimal solutions. To have better performance in energy management, optimization-based strategies were developed. The categories of the existing optimization-based strategies are identified from the latest literature, and a brief study of each strategy is discussed, which consists of the main research ideas, the research focus, advantages, disadvantages and improvements to ameliorate optimality and real-time performance. Deterministic dynamic programming strategy is regarded as a benchmark. Based on neural network and the large data processing technology, data-driven strategies are put forward due to their approximate optimality and high computational efficiency. Finally, the comprehensive performance of each control strategy is analyzed with respect to five aspects. This paper not only provides a comprehensive analysis of energy management control strategies for HEVs, but also presents the emphasis in the future.

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Section: Deterministic Rule-based Strategymentioning

confidence: 99%

Section: Stochastic Dynamic Programming (Sdp)mentioning

confidence: 99%

Section: Stochastic Dynamic Programming (Sdp)mentioning

confidence: 99%

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Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies

Kong

Liang

et al. 2019

Applied Sciences

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“…Generally, a power-split hybrid electric vehicle (HEV) distributes the driving power through one engine and two motor-generators (MGs) coupled with a power-split transmission (PST) [1][2][3]. When operating a power-split HEV, the planetary gear (PG) sets serve as a PST that transfers the engine power to the vehicle through the mechanical path and the electrical path, as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Mode Shift Control for a Hybrid Heavy-Duty Vehicle with Power-Split Transmission

Huang

Chen

et al. 2017

Energies

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Given that power-split transmission (PST) is considered to be a major powertrain technology for hybrid heavy-duty vehicles (HDVs), the development and application of PST in the HDVs make mode shift control an essential aspect of powertrain system design. This paper presents a shift schedule design and torque control strategy for a hybrid HDV with PST during mode shift, intended to reduce the output torque variation and improve the shift quality (SQ). Firstly, detailed dynamic models of the hybrid HDV are developed to analyze the mode shift characteristics. Then, a gear shift schedule calculation method including a dynamic shift schedule and an economic shift schedule is provided. Based on the dynamic models and the designed shift schedule, a mode shift performance simulator is built using MATLAB/Simulink, and simulations are carried out. Through analysis of the dynamic equations, it is seen that the inertia torques of the motor-generator lead to the occurrence of transition torque. To avoid the unwanted transition torque, we use a mode shift control strategy that coordinates the motor-generator torque to compensate for the transition torque. The simulation and experimental results demonstrate that the output torque variation during mode shift is effectively reduced by the proposed control strategy, thereby improving the SQ.

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“…The series-parallel hybrid electric vehicle (SPHEV) is driven by one engine and one traction motor, which are connected by a transmission gear box to distribute power to the wheel along the mechanical power path and the electric power path. 1 The SPHEV has the advantages of both series-type and parallel-type hybrid electric vehicles (HEVs) with the high-efficiency engine operation, independent of the vehicle speed and the electronically controlled continuously variable transmission (ECVT) function, 2,3 which has attracted much attention from the automotive industry because of the great potential to reduce carbon emissions and improve fuel consumption. 4 Furthermore, the SPHEV enables transportation at high efficiencies over a wide variety of operating conditions and provides enhanced tractive effort over a wide range of speeds, and therefore meets the special requirements of heavy-load vehicle, such as wide speed range and large motor power.…”

Section: Introductionmentioning

confidence: 99%

Model reference adaptive control of a series–parallel hybrid electric vehicle during mode shift

Huang

Chen

Langari

2017

Proceedings of the Institution of Mechanical Engineers, Part I:

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Mode shift, as an essential transient state of series–parallel hybrid electric vehicle, might generate torque variation and intensive vehicle jerk due to the discontinuity and nonlinearity of dynamics of the powertrain topology, which results in an unpleasant driving comfort. Therefore, it is necessary to propose a proper control strategy and ensure a smooth transition process for the series–parallel hybrid electric vehicle during mode shift. This article presents a model reference adaptive controller to manage clutch engagement so as to decrease vehicle jerk and clutch frictional loss in a series–parallel hybrid electric vehicle. To design a clutch-coupled transmission system in this article, dynamic equations of a series–parallel hybrid electric vehicle are derived. To avoid the friction-induced discontinuity of clutch frictional torque during mode shift, a model reference adaptive controller based on hyper-stability theory is proposed that coordinately manages the clutch frictional torque. The simulation and the experimental results demonstrate that the proposed model reference adaptive controller in this article can effectively decrease the vehicle jerk and clutch frictional loss when compared with the conventional control method.

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Two-step optimal energy management strategy for single-shaft series-parallel powertrain

Cited by 21 publications

References 25 publications

Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies

Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies

Mode Shift Control for a Hybrid Heavy-Duty Vehicle with Power-Split Transmission

Model reference adaptive control of a series–parallel hybrid electric vehicle during mode shift

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