Cylinder Pressure Signal Research Articles

Recursive Engine In-Cylinder Pressure Reconstruction Using Sensor-Fused Engine Speed.

The engine in-cylinder pressure is a very important parameter for the optimization of internal combustion engines. This paper proposes an alternative recursive Kalman filter-based engine cylinder pressure reconstruction approach using sensor-fused engine speed. In the proposed approach, the fused engine speed is first obtained using the centralized sensor fusion technique, which synthesizes the information from the engine vibration sensor and engine flywheel angular speed sensor. Afterwards, with the fused speed, the engine cylinder pressure signal can be reconstructed by inverse filtering of the engine structural vibration signal. The cylinder pressure reconstruction results of the proposed approach are validated by two combustion indicators, which are pressure peak Pmax and peak location Ploc. Meanwhile, the reconstruction results are compared with the results obtained by the cylinder pressure reconstruction approach using the calculated engine speed. The results of sensor fusion can indicate that the fused speed is smoother when the vibration signal is trusted more. Furthermore, the cylinder pressure reconstruction results can display the relationship between the sensor-fused speed and the cylinder pressure reconstruction accuracy, and with more belief in the vibration signal, the reconstructed results will become better.

Cylinder pressure reconstruction to compensate for crankshaft flexibility based on flexible crank model

Amid escalating economic demands and emissions regulations for internal combustion engines, an array of control strategies grounded in in-cylinder state feedback has been introduced and implemented. The exploration of cylinder state feedback has captured the attention of researchers, and one area of intense focus involves reconstructing cylinder pressure based on engine state information due to its cost-effectiveness. In this paper, we introduce a method for compensating crankshaft flexibility. This method encompasses a crankshaft dynamic model, a 17-dimensional input vector, the PCA (Principal Component Analysis) algorithm, and a signal-layer perceptron. This approach efficiently addresses the impact of flexible crankshaft deformation and ensures high accuracy. The results demonstrate that all R2 values for cylinder pressure signals reconstructed via the proposed method exceed 0.9997, with RMSE predominantly ranging between 0.4 and 0.6 bar. Furthermore, the error in Pmax calculation based on cylinder pressure signals is under 0.04 bar, LPP (Location of Peak Pressure) error is below 2°CA, CA50 error remains under 0.5°CA, and IMEPH error is within 0.3 bar. In summation, the presented method facilitates highly precise in-cylinder pressure reconstruction, satisfies the prerequisites for in-cylinder combustion state feedback.

Simulation Study on Hydraulic Braking Control of Engine Motor of Hybrid Electric Vehicle

Abstract Taking the hybrid electric vehicle as the research object, under the premise of ensuring braking safety, aiming at maximizing the use of motor regenerative braking force and improving the coordination performance of motor hydraulic braking, a simulation study of motor hydraulic braking control based on hybrid electric vehicle engine is proposed. According to the dynamic model and ideal braking force distribution curve of the hybrid electric vehicle, combined with the common idea of electro-hydraulic compound braking force distribution, a three-layer braking control structure of the hybrid electric vehicle is constructed. The management determines the braking intention through the driver's pedal action and calculates the expected torque, and the control layer obtains the target braking force distribution relationship through the logic gate limit control method based on the expected torque. According to the actual motor torque signal fed back by the executive layer and the wheel cylinder pressure signal of the hydraulic braking system, the braking force and regenerative braking force of the hydraulic system are dynamically coordinated and controlled to ensure that the state switching of each component can be rapid, stable, and timely, and the control instruction is transmitted to the motor hydraulic braking system of the executive layer through the vehicle controller to complete the motor hydraulic braking of the hybrid electric vehicle engine. The experimental results show that this method can realize the reasonable distribution of motor hydraulic braking under different braking intensities, different initial braking speeds, and different pedal dip amplitudes, which makes the reaction speed of the hybrid electric vehicle in the braking process faster, the braking switching more stable and safe, effectively improves the energy utilization rate of the hybrid electric vehicle, and ensures the economy and safety of braking control of the hybrid electric vehicle.

Knock recognition of knock sensor signal based on wavelet transform and variational mode decomposition algorithm

Natural gas has become the most widely used alternative energy for engines because of its low emission pollution and sufficient energy reserve. In order to improve the thermal efficiency of the natural gas engine, it is necessary to increase the compression ratio, but knock restricts the increase of the engine compression ratio. The main knock detection method of engine is to use knock sensor. However, the body vibration detected by the knock sensor contains a lot of interference noise, which brings bad effect on the accuracy of knock detection. Thus, in this paper, a knock recognition method based on wavelet transform (WT) and variational mode decomposition (VMD) algorithm is proposed to process the knock sensor signal and extract the knock feature. Based on the test bench of natural gas engine, experimental investigation of knock combustion characteristics was conducted by adjusting the spark timing, and simultaneously collects cylinder pressure signal and knock sensor signal. Burg algorithm based on Auto-regressive (AR) model was used to extract the knock characteristic frequency of the research engine. Then, wavelet transform and variational mode decomposition algorithm were employed to process the knock sensor signal to remove the interference noise and extract the knock feature. Maximum amplitude of pressure oscillation (MAPO) was selected as the knock intensity evaluation index of cylinder pressure signal. Then, taking the evaluation result of the knock intensity of cylinder pressure signal as reference, the root-mean-square of the knock characteristic signal was used as knock intensity evaluation index of knock sensor signal to determine the knock intensity classification of the knock sensor signal. The results show that the proposed method can effectively extract the knock feature from the knock sensor signal and realize the accurate engine knock recognition. The recognition accuracy is higher than the most existing knock recognition methods.

Ion Current Based Data Driven Control of HCCI - the Key to Improve Pressure Based Control Strategies?

Homogeneous charge compression ignition (HCCI) is a low-temperature combustion process that can both increase efficiency and reduce pollutant raw emissions. The process is highly dependent on low-temperature kinetics of the chemical reactions that lead to auto-ignition. Due to strong coupling of consecutive combustion cycles stable control of HCCI is a major challenge. Ion current sensors that detect ionization in the combustion chamber can provide information about chemical reactions taking place inside the cylinder. In contrast, model-based HCCI control strategies, which require accurate models that describe the current cylinder state, are often based only on the cylinder pressure. However, low-temperature chemical kinetics are not sufficiently taken into account by only using the cylinder pressure signal. Therefore, the main hypothesis of this paper is that HCCI strongly depends on the chemical state in the cylinder, which cannot be adequately described by the pressure sensor in the cylinder alone. Hence, a conventional spark plug is used to apply an ion current sensor, which is used alongside the pressure sensor. The aim is to use further information about the state of the chemical mixture for the control of the engine. The features derived from the ion current are integrated into an explicit artificial neural network-based controller. The developed control strategies are then compared against a purely pressure-based controller. Using the ion current features, the standard deviation of the combustion phasing CA50 is reduced by up to 42% compared to the pressure-based approach. The experimental results of this work show that the inclusion of the ion current in the controller provides a significant advantage for low temperature combustion control.

Carbon deposition fault diagnosis of small piston engine based on optimized VMD

Aiming at the small piston engine carbon deposition fault in the process of running, based on the cylinder pressure and cylinder head vibration signal of the engine, a fault diagnosis method combining variational mode decomposition and support vector machine is used to diagnose the engine carbon deposition fault. Firstly, particle swarm optimization algorithm is used to optimize the parameters of the variational mode decomposition. Then, the intrinsic mode function is obtained by processing the pressure signal and cylinder head vibration signal of the engine. Then, the singular spectrum entropy is calculated by singular value decomposition of the intrinsic mode function. Finally, the singular spectrum entropy is input into the support vector machine classifier as the feature data set for training and testing. The results show that this method can identify the carbon deposition fault of the starting motor well, and the accuracy of fault identification and classification of cylinder pressure and cylinder head vibration signal is 98.33 % and 99.17 % respectively, which verifies the effectiveness of this method.

Review on Methodologies Used for Knocking Detection and Intensity Evaluation in Internal Combustion Engines

Abstract: Extracting the knocks characteristics is the key of ignition control in auto-ignition compression engines, especially those severely suffered from abnormal consumption in the engine chamber. Most of the advanced detecting methods of knocking are generally related to the two main variables, including engine cylinder pressure signals and the engine cylinder block vibration signals, commonly measured using piezoelectric accelerometer sensors. Being familiar with different approaches capable of determining the engine knocking occurrence probability as well as providing suitable methods for knock intensity evaluation is significantly helpful to propose a comprehensive predicting model for measuring the level of oscillation pressure waves frequencies originated by the knocks. Following by predicting the knock intensity and extracting the knock feature, optimizing the engine operation in terms of fuel consumption, thermal efficiencies, and lifetime duration would be possible. For this aim, the current paper is addressed with overviewing upon the researches performed in the regard of developing high accurate models and optimized knock detection approaches. Keywords: auto-ignition, Internal combustion engine, Knocking detection approach, Knock intensity evaluation.

Toward a cylinder pressure signals-based active synthesis algorithm for engine sound

The active sound design (ASD) technology is one of the most flexible methods to achieve the diverse design of automobile sound, in which the active sound synthesis algorithm is essential. However, in previous works, the discontinuity phenomena always appear in the synthesized sound. Thus, a new method of active engine sound synthesis based on cylinder pressure signals is proposed in this paper for the sound synthesis algorithm of ASD technique. Firstly, the index matrix of sound grains is constructed based on the periodicity of engine cylinder pressure signal. Then the frame length and frame shift are defined by tracing the time step of Revolutions Per Minute (RPM) of engine, and the hamming window function is constructed to extract sound grains. Subsequently, the overlap-and-add (OLA) method is used to realize the synthesis of engine sounds with different durations. In addition, the code program of proposed algorithm is written and the ASD prototype system is built respectively to synthesize the engine sounds. Finally, a similarity evaluation metric is defined to verify the effectiveness of proposed algorithm. The results show that our algorithm not only restitutes the main features of original sound, but also satisfies both the real-time feedback on the driving status and the continuity of synthesized sound. It contributes to the development of real-time ASD engineering for automobiles.

The statistical characteristics of knock signal waveforms

Individual instances of the knock resonant response are easy to acquire but these are subject to noise and vary considerably from cycle to cycle due to random variations in the knock process. This work provides a new way to quantify and model the stochastic properties of knock signals, capturing both the time domain resonant characteristics within a cycle as well as the random variations from cycle to cycle. A new phase alignment method enables the ensemble mean knock waveform to be identified from the data which also removes noise components without the need for narrowband filtering. This ensemble waveform shows the empirical characteristics of knock onset, decay, and frequency slurring within the cycle as the gas expands and cools. The phase-aligned cyclic variations of the knock waveform are also shown to approximate a (time-varying) dual-Gaussian distribution, and fitting such a model to the data enables the statistical properties of the dataset as a whole to be decomposed into separate knocking and non-knocking populations providing further insight into the knock process. The technique is applied both to filtered cylinder pressure signals and to accelerometer-based knock signals, and the results are compared and contrasted.

Recognition of cutting rock hardness by hydraulic cylinder pressure

The online identification of rock hardness is the basis to adjust the rotation velocity and swing velocity of cutting head in real time. To effectively identify the hardness of rock around roadway under different cutting conditions, a novel method based on the hydraulic cylinder pressure signal is proposed. Here, we analyze the changing rule of cutting head load under different rock hardness through dynamic simulation, and the transfer characteristics between cutting dynamic load and the hydraulic cylinder pressure. On this basis, a function model between the hydraulic cylinder pressure and the protodyakonov scale of rock hardness is constructed to identify the rock hardness. The average recognition accuracy of the heavy-duty roadheader in the test is 92.01%. The experimental results indicate that the model can effectively identify the rock hardness under different cutting conditions. This study can be used for online identification of rock hardness and provide reliable basis for the automatic control of roadheader.

The statistical properties of raw knock signal time histories

Traditional processing of knock signals reduces each recorded time history to a single scalar knock intensity metric for each engine firing, thereby losing all information relating to the phasing and evolution of knock over the combustion period. In this work, a statistical analysis of raw knock cylinder pressure and accelerometer signal time histories has been performed showing how the nonstationary stochastic characteristics of these signals evolve as a function of crank angle, and as a function of spark timing. The data are shown to closely approximate a cyclically independent process whose 2-dimensional covariance function captures both the non-stationary deterministic processes taking place within a cycle as well as the stationary random variations from one cycle to the next. A (time-varying) dual-Gaussian model for the distribution of these cyclic variations was fitted to the data, thereby enabling them to be characterized as the sum of knocking and non-knocking populations. The parameters of the model, which describe these populations separately, provide further empirical insight into the knock process.

Analysis of ion current signal during negative valve overlap of HCCI combustion with high compression ratio

Homogenous charge compression ignition (HCCI) combustion is a low temperature combustion process which combines high combustion efficiency with ultra-low [Formula: see text] raw emissions. Steep increases of the in-cylinder pressure and unstable combustion sequences at the limits of the operating range can damage the engine and limit the use of HCCI to part load operation. This can be done using closed loop combustion control based on combustion parameters like the indicated mean effective pressure and the combustion phasing. Since in-cylinder pressure sensors are expensive components and therefore not suitable for series application, ion current sensors can be used as an additional source of information about the combustion. Combustion analysis using methods similar to those used in pressure based measurements can be implemented using an online analysis of the ion current signal. In this study, the ion current sensor will be examined for its suitability for combustion control under HCCI conditions with lean air/fuel ratios and high compression ratios. Research has found that the ion current signal is strongly depended on the boundary conditions. Especially the air/fuel ratio which plays an important role for signal strength during the combustion process. When using valve timings with negative valve overlap in combination with a fuel pre-injection, a further peak of the ion current signal close to the gas exchange top dead center can be found in addition to the one during combustion. At the same time, it is hard to extract information from the cylinder pressure signal during NVO. Under lean conditions this peak even exceeds the signal during combustion. This study analyzes the ion current signal during NVO and its potential to be used for future combustion control concepts. The ion current signal shows potential to stabilize HCCI combustion at high loads. However, the prediction of late combustion cycles is still challenging.

Development and Application of Ion Current/Cylinder Pressure Cooperative Combustion Diagnosis and Control System

The application of advanced technologies for engine efficiency improvement and emissions reduction also increase the occurrence possibility of abnormal combustions such as incomplete combustion, misfire, knock or pre-ignition. Novel promising combustion modes, which are basically dominated by chemical reaction kinetics show a major difficulty in combustion control. The challenge in precise combustion control is hard to overcome by the traditional engine map-based control method because it cannot monitor the combustion state of each cycle, hence, real-time cycle-resolved in-cylinder combustion diagnosis and control are required. In the past, cylinder pressure and ion current sensors, as the two most commonly used sensors for in-cylinder combustion diagnosis and control, have enjoyed a seemingly competitive relationship, so all related researches only use one of the sensors. However, these two sensors have their own unique features. In this study, the idea is to combine the information obtained from both sensors. At first, two kinds of ion current detection system are comprehensively introduced and compared at the hardware level and signal level. The most promising variant (the DC-Power ion current detection system) is selected for the subsequent experiments. Then, the concept of ion current/cylinder pressure cooperative combustion diagnosis and control system is illustrated and implemented on the engine prototyping control unit. One application case of employing this system for homogenous charge compression ignition abnormal combustion control and its stability improvement is introduced. The results show that a combination of ion current and cylinder pressure signals can provide richer and also necessary information for combustion control. Finally, ion current and cylinder pressure signals are employed as inputs of artificial neural network (ANN) models for combustion prediction. The results show that the combustion prediction performance is better when the inputs are a combination of both signals, instead of using only one of them. This offline analysis proves the feasibility of using an ANN-based model whose inputs are a combination of ion current and pressure signals for better prediction accuracy.

Trapped mass estimation in automotive diesel engines based on in‐cylinder pressure signal projection

AbstractCylinder pressure signal provides key feedback information that allows for several engine monitoring and control capabilities. During the last few years, the use of cylinder pressure traces for advanced combustion strategies has become the focus of several research works. This is primarily due to the availability of reliable and inexpensive cylinder pressure sensors with expected durability that meets the vehicle lifetime. In this context, several approaches have been proposed to determine the engine trapped mass from the cylinder pressure measurements. A recent innovative approach for trapped charge determination based on a two‐dimensional graphical signature is proposed in a previous work. The resulting estimator uses cylinder pressure signal as a unique input and allows to deduce the trapped mass on a cycle by cycle basis in steady and transient operating conditions. It has been validated in a wide range of engine operating conditions using instrumentation and industrial cylinder pressure sensors. This paper provides a theoretical framework and in‐depth analysis for the signature based trapped mass estimator. A state‐space model for in‐cylinder conditions during the compression phase that complies with the signature modeling structure is developed. Extensive numerical investigations using an experimentally validated simulation platform are then performed. The objective is to select the optimal signature generation interval that reduces the impact of cycle to cycle fluctuations in terms of intake valve closing temperature and polytropic index. The obtained results are promising and clearly show the performance and robustness of the signature based trapped mass estimator that can provide relevant feedback information for adaptive engine control systems. It can be easily implemented for real‐time monitoring and control in industrial automotive applications.

Research on curve smoothing algorithm for diesel indicator diagram

The algorithm of curve smoothing for diesel indicator diagram is introduced, and the concrete implementation process is expounded in detail. First, the multi cyclic mean value operation of indicator diagram data is performed. By probable error of calculating indicator diagram data, if probable error is greater than a specific value, and the predicted value is used instead of the singular point data. The average operation of the indicator diagram data is processed again. Finally, the five-point cubic smoothing method is used to smooth the indicator curve, and the smooth, continuous and gradual indicator diagram curve is obtained. The experiments show that the algorithm described in the paper can effectively filter the noise of cylinder pressure signal, and obtain a smooth indicator diagram curve, which verifies the effectiveness of the algorithm described in the paper.

Semi-physical state and parameter estimation of diesel combustion phases for real-time applications

A model-based methodology is presented, which allows the estimation of the characteristic phases of diesel combustion using a semi-physical model approach combined with state and parameter estimation through extended Kalman filtering. The physical relation between the fuel injection and the characteristic diesel combustion phases, such as premixed, diffusive combustion and burn-out, are modeled separately by linear dynamic transfer functions formulated in crank angle frequency domain and transformed into state space representation. The resulting state variables are the released burning energy and its derivatives of each combustion phase. Associated crank angle constants determine the dynamics of the combustion phases and represent the rate parameters to be estimated. By incorporating further physical assumptions regarding the fuel path and air–fuel-mixing dynamics, the combustion phase parameters are estimated online for each working cycle. Cylinder pressure signals and online combustion analysis are used to determine the burn rate of the diesel engine at the test bench. Investigations have shown that the estimated rate parameters depend on the current engine operation point. They are estimated during measurements and stored in lookup tables through an online-learning method based on a fast recursive least squares estimation algorithm.

Combustion and Performance Study of Low-Displacement Compression Ignition Engines Operating with Diesel–Biodiesel Blends

This study investigated the influence of different biodiesel blends produced from residual sunflower oil and palm oil from agroindustry liquid waste on the characteristics of the combustion process, performance, and emissions in a single-cylinder diesel engine. For the analysis of the combustion process, a diagnostic model was developed based on the cylinder pressure signal, which allows the calculation of the heat release rate, the accumulated heat rate, and the temperature in the combustion chamber. This is to assess the influence of these parameters on engine emissions. The experiments on the diesel engine were carried out using five types of fuel: conventional diesel, two biodiesel blends of residual palm oil (PB5 and PB10), and two biodiesel blends formed with palm oil and sunflower oil residues (PB5SB5 and PB10SB5). The engine was running in four different modes, which covered its entire operating area. Experimental results show that the in-cylinder pressure curves decrease as the percentage of biodiesel in the fuel increases. Similarly, the results showed a decrease in the heat release rate for biodiesel blends. The diagrams of the accumulated heat release curves were larger for fuels with higher biodiesel content. This effect is reflected in the thermal efficiency of biodiesel blends since the maximum thermal efficiencies were 29.4%, 30%, 30.6%, 31.2%, and 31.8% for PB10SB5, PB5SB5, PB10, PB5, and diesel, respectively. The emission analysis showed that the blends of biodiesel PB5SB5 and PB10SB allowed a greater reduction in the emissions of CO, CO2, HC, and opacity of smoke in all the modes of operation tested, in comparison with the blends of biodiesel PB5 and PB10. However, NOx emissions increased. In general, biodiesel with the percentage of residual sunflower oil does not cause a significant change in the combustion process and engine performance, when compared to biodiesel that includes only residual palm oil.

Virtual Engine In-Cylinder Pressure Sensor for Automobiles and Agricultural Tractors

The cylinder pressure signal is a very useful indicator for advanced high-performance internal-combustion engines equipped in modern automobiles and agricultural tractors. In this paper, a previous framework for the cylinder pressure estimation is conducted based on a transfer path model (between the cylinder pressure signal and the vibration signal) identified in frequency domain. The main idea behind the previous framework is based on the Kalman filter (of which the input is the engine structure vibration) for the augmented model formed by augmenting the cylinder pressure signal model with delay blocks and the transfer path model identified in time domain. Two combustion metrics, pressure peak Pmax and peak location Ploc, are used for evaluating the estimation framework under the transfer path identified in frequency domain. The estimation results are compared with both experimental data collected from a four-cylinder diesel engine and previous work results, and illustrate the effectiveness of the newly identified transfer path.

Relations between ion signal and flame propagation in cylinder of a rapid compression machine

Internal combustion engine diagnostics using traditional methods of cylinder pressure signal processing limits the amount of information available about the combustion process. It is necessary to conduct research in order to obtain more precise information – increasing the combustion process diagnosis potential. One such suggestion is the use of an ionization signal and an attempt to link it to the flame development during combustion of gaseous fuels. The article attempts to identify such a relationship using a rapid compression machine due to optical access it provides to the combustion chamber. As a result of the research, the relationships between the ionization voltage (chemical and thermal) of the first combustion phase and the corresponding flame development rates were determined. A relatively high coefficient of determination value was obtained for both relations, which indicates the possibility of obtaining diagnostic information about the combustion process from the ionization signal.

Evaluation of the combustion-induced noise and vibration using coherence and wavelet coherence estimates in a diesel engine

The understanding of noise generation and source identification is vital in noise control. This research was conducted to experimentally evaluate combustion-induced noise and vibration using coherence and wavelet coherence estimates. A single-cylinder direct-injection diesel engine was chosen for experimental investigation. The independent variables for conducting experiments were injection timing with five levels of 22, 27, 32 (normal), 37, and 42 crank angles before the top dead center, and also the engine torque load with four levels of 55%, 70%, 85%, and 100% of the rated value. The signals of cylinder pressure, liner acceleration, and radiated sound pressure of the test engine were measured and recorded. Then, coherency and wavelet coherency experiments were carried out between cylinder pressure and liner acceleration, cylinder pressure and sound pressure, and liner acceleration and sound pressure signals in MATLAB software. The results indicated that increasing load would increase wavelet coherency between cylinder pressure and liner acceleration signals at frequencies higher than 1 kHz. The coherent regions between cylinder pressure and sound pressure signals were mainly at frequencies higher than 1 kHz while advancing the fuel injection timing had shifted the coherency toward lower frequencies. In general, with advancing injection timing, the coherent regions between liner acceleration and sound pressure signals have appeared at broader time ranges, especially at frequencies between 100 and 500 Hz. Comparing the results of the wavelet coherency and coherency tests, we concluded that wavelet coherency is a more accurate and descriptive tool in evaluating the combustion-induced noise and vibration.