Engine modelling architecture study for hybrid electric vehicle diagnosis application

Wan, Peng; Liu, Bolan; Li, Ben; Liu, Fanshuo; Zhang, Junwei; Fan, Wenhao; Tang, Jingxian

doi:10.1016/j.energy.2023.128408

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…If the value of signal level exceeds that of the allowable threshold, the ECU will consider that signal as a fault one and sends a fault code to its memory. Nonetheless, the method has not been able to reach high fault detection rates (FDRs) and low false alarm rates (FARs) because of its poor anti-jamming ability [9,10]. Compared to the FDD methods mentioned earlier, the Orthonormal Subspace Analysis (OSA) method relies solely on historical data, without the need for complex HEV models.…”

Section: Introductionmentioning

confidence: 99%

Fault detection and separation of hybrid electric vehicles based on kernel orthogonal subspace analysis

Wang,

Deprizon,

Peng

et al. 2023

J Appl Eng Science

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Driving quality and vehicles safety of hybrid electric vehicles (HEVs) are two hot-topic issues in automobile technology. Nowadays, research focuses to more intelligent and convenient HEVs fault detection methods. This paper will focus on the fault detection of HEV powertrain system with a data-driven algorithm. Orthonormal subspace analysis (OSA) is a newly proposed data-driven method which adds the ability of fault separation. Nonetheless, the linear OSA algorithm cannot effectively detect powertrain system faults, since these faults present complex nonlinear characteristics. A new kernel OSA (KOSA) method is proposed to transform the nonlinear problem into a linear problem through the mapping of kernel function and the dimensionality reduction technique of OSA. Testing results on a nonlinear model and real samples of XMQ6127AGCHEVN61 HEV show that KOSA address the nonlinear problems and it performs better than OSA and kernel principal component analysis (KPCA)

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Section: Introductionmentioning

confidence: 99%

Fault detection and separation of hybrid electric vehicles based on kernel orthogonal subspace analysis

Wang,

Deprizon,

Peng

et al. 2023

J Appl Eng Science

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Transient Performance of Gas-Engine-Based Power System on Ships: An Overview of Modeling, Optimization, and Applications

Wu,

Li,

Chen

et al. 2023

JMSE

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Liquefied natural gas (LNG) is widely regarded as the midterm solution toward zero-carbon transportation at sea. However, further applications of gas engines are challenging due to their weak dynamic load performance. Therefore, the comprehension of and improvements in the dynamic performance of gas-engine-based power systems are necessary and urgent. A detailed review of research on mechanisms, modeling, and optimization is indispensable to summarize current studies and solutions. Developments in engine air-path systems and power system load control have been summarized and compared. Mechanism studies and modeling methods for engine dynamic performance were investigated and concluded considering the trade-off between precision and simulation cost. Beyond existing studies, this review provides insights into the challenges and potential pathways for future applications in decarbonization and energy diversification. For further utilization of clean fuels, like ammonia and hydrogen, the need for advanced air–fuel ratio control becomes apparent. These measures should be grounded in a deep understanding of current gas engines and the combustion characteristics of new fuels. Additionally, the inherent low inertia feature of electric power systems, and consequently the weak dynamic performance when adopting renewable energies, must be considered and studied to ensure system reliability and safety during transient conditions.

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A Deviation-Based Centroid Displacement Method for Combustion Parameters Acquisition

Wei,

Yao,

et al. 2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">The absence of combustion information continues to be one of the key obstacles to the intelligent development of engines. Currently, the cost of integrating cylinder pressure sensors remains too high, prompting attention to methods for extracting combustion information from existing sensing data. Mean-value combustion models for engines are unable to capture changes of combustion parameters. Furthermore, the methods of reconstructing combustion information using sensor signals mainly depend on the working state of the sensors, and the reliability of reconstructed values is directly influenced by sensor malfunctions. Due to the concentration of operating conditions of hybrid vehicles, the reliability of priori calibration map has increased. Therefore, a combustion information reconstruction method based on priori calibration information and the fused feature deviations of existing sensing signals is proposed and named the "Deviation-based Centroid Displacement Method" (DCDM). The method based on priori calibration information, extract features of crankshaft transient angular velocity and knock signals. Using the parameter identification method, it acquires transient values of combustion parameters reconstructed based on various signal features. The fused deviation between transient values and calibration values is calculated using the Kalman filter and employed to adjust the priori values, realizing the computation of transient combustion parameters. A test platform for reconstructing combustion information is established in conjunction with an engine bench. The DCDM model is verified under 11 operating conditions, with the maximum error between the CA10, CA50 and CA90 computed by the DCDM model and experimental values being less than 2 °CA and the average error being less than 1 °CA, indicating high accuracy of the model. The Minkowski distance is less than 0.7, and the model distance is less than 0.3, demonstrating a good real-time performance and consistency of changes.</div></div>

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Engine modelling architecture study for hybrid electric vehicle diagnosis application

Cited by 3 publications

References 29 publications

Fault detection and separation of hybrid electric vehicles based on kernel orthogonal subspace analysis

Fault detection and separation of hybrid electric vehicles based on kernel orthogonal subspace analysis

Transient Performance of Gas-Engine-Based Power System on Ships: An Overview of Modeling, Optimization, and Applications

A Deviation-Based Centroid Displacement Method for Combustion Parameters Acquisition

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