Driving Pattern Recognition for Control of Hybrid Electric Trucks

Lin, C.C.; Jeon, Soonil; Peng, Huei; Lee, Jang Moo

doi:10.1080/00423110412331291553

Cited by 99 publications

(47 citation statements)

References 13 publications

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“…The methods for data analysis are detailed in [53]. In [43], an adaptive power management based on driving pattern recognition is presented. The driving pattern recognition algorithm classifies the given representative drive cycles (RDPs) based on low, medium and high power demands and creates driving patterns satisfying the characteristic parameters obtained from the driver velocity.…”

Section: Power Management Optimization With Unknown Velocity Inputmentioning

confidence: 99%

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Optimal Rule-Based Power Management for Online, Real-Time Applications in HEVs with Multiple Sources and Objectives: A Review

Moulik

Söffker

2015

Energies

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The field of hybrid vehicles has undergone intensive research and development, primarily due to the increasing concern of depleting resources and increasing pollution. In order to investigate further options to optimize the performance of hybrid vehicles with regards to different criteria, such as fuel economy, battery aging, etc., a detailed state-of-the-art review is presented in this contribution. Different power management and optimization techniques are discussed focusing on rule-based power management and multi-objective optimization techniques. The extent of rule-based power management and optimization in solving battery aging issues is investigated along with an implementation in real-time driving scenarios where no pre-defined drive cycle is followed. The goal of this paper is to illustrate the significance and applications of rule-based power management optimization based on previous contributions.

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Section: Power Management Optimization With Unknown Velocity Inputmentioning

confidence: 99%

“…In [18,21,43], for example, the optimal division of output torque is based on a specific driving pattern, and in [44], DP is considered for the optimization of several respective driving patterns. On the other hand, in [45], a prediction of the future driving pattern is considered based on past data.…”

Section: Introductionmentioning

confidence: 99%

Optimal Rule-Based Power Management for Online, Real-Time Applications in HEVs with Multiple Sources and Objectives: A Review

Moulik

Söffker

2015

Energies

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“…As the second type of driving pattern identification method relies only on the theoretical study of the control algorithms, which can be applied more easily, this paper focuses only on method two [9]. Lin et al [5] and Won et al [10] have both realized a kind of DPR based on the analysis of feature parameters extracted from the velocity data. The control effects are impressive.…”

Section: Introductionmentioning

confidence: 99%

“…Since the powertrain control strategy plays a very important role in improving the performance of HEVs, the study of new control theories that can be applied to the power trains of HEVs is very significant [1,2]. Many control strategies, such as fuzzy control, logic threshold control, dynamic programming and artificial neural network control, have been developed and successfully applied in HEVs [2][3][4][5][6][7][8][9][10]. Comparative studies have shown that the flexibility of the control strategies is very important for the improvement of the control effects [2,3].…”

Section: Introductionmentioning

confidence: 99%

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A Method for Identification of Driving Patterns in Hybrid Electric Vehicles Based on a LVQ Neural Network

Sun

Zhang

2012

Energies

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Driving patterns exert an important influence on the fuel economy of vehicles, especially hybrid electric vehicles. This paper aims to build a method to identify driving patterns with enough accuracy and less sampling time compared than other driving pattern recognition algorithms. Firstly a driving pattern identifier based on a Learning Vector Quantization neural network is established to analyze six selected representative standard driving cycles. Micro-trip extraction and Principal Component Analysis methods are applied to ensure the magnitude and diversity of the training samples. Then via Matlab/Simulink, sample training simulation is conducted to determine the minimum neuron number of the Learning Vector Quantization neural network and, as a result, to help simplify the identifier model structure and reduce the data convergence time. Simulation results have proved the feasibility of this method, which decreases the sampling window length from about 250-300 s to 120 s with an acceptable accuracy. The driving pattern identifier is further used in an optimized co-simulation together with a parallel hybrid vehicle model and improves the fuel economy by about 8%.

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Hierarchical Model Predictive Control for Parallel Hybrid Electrical Vehicles

Song

et al. 2018

Asian Journal of Control

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One purpose of the control strategy for a parallel hybrid electric vehicle (PHEV) is to control the state of charge (SOC) of the battery to achieve a maximum powertrain efficiency. Because of the nonlinearity of the powertrain, the control strategy should implement nonlinear optimization in real time. This paper presents a new hierarchical optimal control design to execute real-time optimization on the basis of a model predictive control concept. The proposed control architecture suggests a two-layer control subjected to the related constraints according to the changing rate of different parameters. The prime controller optimizes the best trajectory of the state of charge according to the load detected by a load prediction module, while the second controller will apply the output signal from the prime controller along with the signal from the prediction module and the driver inputs to control the powertrain. Additionally, a new methodology to predict the future load imposed on the wheels is introduced. Through simulation and experiment results, it is shown that the proposed prediction control can effectively reduce fuel consumption and exhaust emission.

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Driving Pattern Recognition for Control of Hybrid Electric Trucks

Cited by 99 publications

References 13 publications

Optimal Rule-Based Power Management for Online, Real-Time Applications in HEVs with Multiple Sources and Objectives: A Review

Optimal Rule-Based Power Management for Online, Real-Time Applications in HEVs with Multiple Sources and Objectives: A Review

A Method for Identification of Driving Patterns in Hybrid Electric Vehicles Based on a LVQ Neural Network

Hierarchical Model Predictive Control for Parallel Hybrid Electrical Vehicles

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