Engine life prediction method and device, vehicle and electronic equipment

Abstract

The invention relates to an engine life prediction method and device, a vehicle and electronic equipment, and relates to the technical field of engines. The method comprises the steps that the performance degradation degree of the engine is determined; the performance deterioration degree comprises at least two of the following: a first performance deterioration degree in fuel economic performance, a second performance deterioration degree in fuel atomization performance, a third performance deterioration degree in lubricating performance and a fourth performance deterioration degree in cooling performance; the first performance deterioration degree and the second performance deterioration degree are determined based on fuel economy parameters of the engine, the third performance deterioration degree is determined based on lubricity parameters of the engine, and the fourth performance deterioration degree is determined based on cooling parameters of the engine; based on the performance degradation degree, the residual life of the engine is predicted, the engine life prediction efficiency and precision can be improved, and the operation safety of the engine is improved.

Description

Technical Field

[0001] This application relates to the field of engine technology, specifically to an engine life prediction method, device, vehicle, and electronic equipment. Background Technology

[0002] As the core power source in automobiles and many other fields, the engine has a complex internal structure and diverse working environment. Therefore, accurately assessing the engine's lifespan is of great help in optimizing the engine's operation and formulating subsequent maintenance strategies, which can effectively improve the engine's reliability, economy, and safety.

[0003] Traditional engine life assessment primarily relies on technicians inspecting key parameters such as the vehicle's fuel system, lubricating oil condition, and coolant temperature, combined with fault code reading and subjective noise assessment. However, this method has several significant drawbacks. From an efficiency perspective, the entire inspection and assessment process is time-consuming, requiring technicians to test and analyze each indicator individually, resulting in lengthy repair cycles and failing to meet users' demands for rapid service. In terms of cost, the need for specialized equipment and substantial manpower keeps repair costs high, increasing the financial burden on users and limiting the overall efficiency improvement of the automotive aftermarket service industry. Summary of the Invention

[0004] One of the purposes of this application is to provide an engine life prediction method, device, vehicle, and electronic equipment that can improve the efficiency and accuracy of engine life prediction and enhance engine operational safety.

[0005] To achieve the above objectives, the technical solution adopted in this application is as follows:

[0006] According to a first aspect provided in this application, an engine life prediction method is provided, the method comprising: determining the performance degradation degree of the engine; the performance degradation degree includes at least two of the following: a first performance degradation degree in fuel economy performance, a second performance degradation degree in fuel atomization performance, a third performance degradation degree in lubrication performance, and a fourth performance degradation degree in cooling performance; the first and second performance degradation degrees are determined based on the engine's fuel economy parameters, the third performance degradation degree is determined based on the engine's lubrication parameters, and the fourth performance degradation degree is determined based on the engine's cooling parameters; and predicting the remaining life of the engine based on the performance degradation degree.

[0007] Based on the aforementioned technical methods, operating parameters such as fuel economy, lubrication, and cooling are directly related to engine wear, aging, and failure mechanisms. Therefore, this application combines multi-dimensional operating parameters such as fuel economy, lubrication, and cooling to more accurately reflect the true deterioration state of the engine, thereby improving the accuracy of remaining life prediction. Furthermore, real-time monitoring of performance degradation can provide timely warnings of potential engine risks, enabling proactive maintenance to address these risks, reduce repair costs and downtime losses, and significantly reduce the probability of severe engine failures through proactive intervention, thus improving engine operational safety.

[0008] In one possible approach, fuel economy parameters include fuel consumption. Based on this, the degree of engine performance degradation is determined, including: determining the engine's fuel consumption rate based on engine torque, speed, and fuel consumption; and determining the engine's first degree of performance degradation in terms of fuel economy based on the fuel consumption rate.

[0009] Based on the aforementioned technical methods, since fuel consumption is greatly affected by operating conditions such as load, speed, and torque, directly using fuel consumption to judge the degree of performance degradation may lead to misjudgment. Fuel consumption rate, however, directly reflects the engine's efficiency in converting the chemical energy of fuel into mechanical work. The initial performance degradation degree determined based on fuel consumption rate can effectively eliminate the influence of operating condition fluctuations, accurately reflect the true degree of degradation in the engine's fuel economy performance, and provide a reliable basis for predicting the engine's remaining life.

[0010] In one possible approach, fuel economy parameters include fuel consumption per 100 kilometers, which refers to the amount of fuel consumed by the vehicle over 100 kilometers. Based on this, the engine's performance degradation is determined, including: determining the engine's fuel consumption rate based on vehicle speed, fuel density, fuel consumption per 100 kilometers, and engine output power. Based on the fuel consumption rate, the engine's first degree of performance degradation in terms of fuel economy is determined.

[0011] Based on the aforementioned technical methods, fuel consumption per 100 kilometers is significantly affected by operating conditions such as vehicle speed and load (e.g., there is a significant difference in fuel consumption per 100 kilometers between highway driving and urban congestion). Directly using it to assess performance degradation may lead to misjudgments. Fuel consumption rate, however, is essentially a quantitative indicator of the efficiency of conversion between fuel chemical energy and mechanical work; changes in its value directly reflect a decrease in engine combustion efficiency or an increase in mechanical losses. This application introduces vehicle speed, fuel density, and engine output power to convert fuel consumption per 100 kilometers into fuel consumption rate, which can reduce interference from different driving conditions and thus improve the accuracy of the first performance degradation level.

[0012] One possible approach involves determining the engine's first degree of performance degradation in fuel economy based on fuel consumption rates, including: determining each difference between historical fuel consumption rates and the current fuel consumption rate; determining each ratio between each difference and the current fuel consumption rate; and determining the average of these ratios as the engine's first degree of performance degradation in fuel economy.

[0013] Based on the aforementioned technical methods, fuel consumption rate is a core indicator for measuring engine fuel economy. By analyzing the deviation between the current fuel consumption rate and various historical fuel consumption rates, the trend of fuel economy performance degradation can be accurately determined, providing a basis for fault diagnosis and life prediction.

[0014] In one possible approach, fuel economy parameters include fuel rail pressure. Based on this, the degree of engine performance degradation is determined, including: determining a second degree of performance degradation in fuel atomization performance based on fuel rail pressure.

[0015] Based on the aforementioned technical methods, fuel rail pressure is a key parameter of the combustion system, directly determining fuel atomization quality. Therefore, this application can quickly assess the degree of degradation in engine fuel atomization performance by comparing the deviation between the current fuel rail pressure and various fuel rail pressures. Furthermore, analyzing the degree of degradation can also provide a basis for optimizing fuel atomization performance.

[0016] In one possible approach, lubrication parameters include oil pressure. Based on this, the degree of engine performance degradation is determined, including: determining the third performance degradation of the engine in terms of lubrication performance based on oil pressure.

[0017] Based on the aforementioned technical methods, oil pressure is a core parameter of the lubrication system. A drop in oil pressure leads to poor lubrication, which in turn increases the risk of wear on various engine components. Therefore, by comparing the current oil pressure with historical oil pressures, the degree of deterioration in engine lubrication performance can be quickly assessed. Furthermore, real-time monitoring of this third performance degradation can provide early warnings, reducing maintenance costs and mitigating unforeseen risks.

[0018] In one possible approach, cooling parameters include coolant temperature. Based on this, the degree of engine performance degradation is determined, including: determining a fourth degree of engine performance degradation in terms of cooling performance based on coolant temperature.

[0019] Based on the aforementioned technical methods, the core function of the cooling system is to maintain the engine coolant temperature within the optimal range. Excessive coolant temperature leads to overheating, reduced mechanical strength, and lubrication failure; conversely, insufficient coolant temperature reduces thermal efficiency and increases fuel consumption. Therefore, by calculating the deviation between the current and historical coolant temperatures, the degree of deterioration in engine cooling performance can be reflected. Furthermore, real-time monitoring of this performance degradation can provide early warnings, reducing the risk of major malfunctions and maintenance costs.

[0020] In one possible approach, the engine performance degradation also includes: a fifth performance degradation in control response performance determined based on phaser response time; and a sixth performance degradation in intake performance determined based on intake manifold pressure.

[0021] Based on the aforementioned technical methods, the phaser, as a core component of the variable valve timing system, directly determines the timeliness and accuracy of valve timing adjustment (response delay leads to lag in power output and increased fuel consumption). By calculating the deviation between the current response time and historical response time, control response performance issues such as phaser sticking and insufficient oil pressure can be accurately identified. Intake manifold pressure is a core indicator reflecting intake volume, directly affecting air-fuel ratio accuracy and combustion efficiency. By calculating the deviation between the current intake manifold pressure and historical intake manifold pressure, the degree of deterioration in engine intake performance can be reflected.

[0022] In one possible approach, predicting the remaining life of an engine based on performance degradation includes: in response to determining that each performance degradation level of the engine is less than a corresponding set performance degradation threshold, determining the engine's wear-out life based on each performance degradation level and the engine's cumulative usage time; and determining the engine's remaining life based on the wear-out life and the engine's design life.

[0023] Based on the aforementioned technical means, the fact that all performance degradation degrees of the engine in this application are less than the corresponding set performance degradation degree thresholds indicates that the engine is in a controllable degradation stage with no major failure risk. Furthermore, each performance degradation degree directly quantifies the degree of degradation of each core system of the engine. By comprehensively analyzing multi-dimensional degradation degrees and cumulative usage time, the cumulative impact of degradation of each system on the overall lifespan can be fully reflected, avoiding prediction bias caused by latent degradation of a particular system (such as abnormal intake manifold pressure), and making the lifespan prediction results closer to the engine's actual condition.

[0024] In one possible approach, the method further includes: in response to determining that the target performance degradation degree is greater than or equal to a corresponding set performance degradation degree threshold, generating a maintenance suggestion based on the target performance degradation degree to prompt for engine maintenance based on the maintenance suggestion; wherein the target performance degradation degree is any performance degradation degree in the engine.

[0025] Based on the aforementioned technical means, a target degradation degree greater than or equal to the corresponding set performance degradation degree threshold is an early signal of engine failure. Therefore, the maintenance suggestions generated by this application when the target performance degradation degree exceeds the standard can not only provide early warning and interception of faults, but also accurately match the root cause of degradation, improve maintenance efficiency and accuracy, and ensure engine operating safety.

[0026] According to a second aspect provided in this application, an engine life prediction device is provided, the device comprising: a determination unit and a prediction unit.

[0027] A determining unit is used to determine the performance degradation degree of the engine; the performance degradation degree includes at least two of the following: a first performance degradation degree in fuel economy, a second performance degradation degree in fuel atomization, a third performance degradation degree in lubrication, and a fourth performance degradation degree in cooling; the first and second performance degradation degrees are determined based on the engine's fuel economy parameters, the third performance degradation degree is determined based on the engine's lubrication parameters, and the fourth performance degradation degree is determined based on the engine's cooling parameters.

[0028] The prediction unit is used to predict the remaining life of the engine based on the degree of performance degradation.

[0029] In one possible embodiment, the determining unit further includes a first determining subunit and a second determining subunit. The first determining subunit is used to determine the engine's fuel consumption rate based on the engine's torque, speed, and fuel consumption. The second determining subunit is used to determine a first degree of performance degradation in the engine's fuel economy based on the fuel consumption rate.

[0030] In one possible approach, the first determining subunit is further configured to determine the engine's fuel consumption rate based on vehicle speed, fuel density, fuel consumption per 100 kilometers, and engine output power. The second determining subunit is further configured to determine a first degree of performance degradation in the engine's fuel economy based on the fuel consumption rate.

[0031] In one possible approach, the second determining subunit is specifically used to determine each difference between each historical fuel consumption rate and the fuel consumption rate, then determine each ratio between each difference and the fuel consumption rate, and determine the average of each ratio as the first performance degradation degree of the engine in terms of fuel economy.

[0032] In one possible approach, the determining unit is specifically used to determine a second performance degradation of the engine in terms of fuel atomization performance, based on the fuel rail pressure.

[0033] In one possible approach, the determining unit is further used to determine the third performance degradation degree of the engine in terms of lubrication performance based on oil pressure.

[0034] In one possible approach, the determining unit is further used to determine the fourth performance degradation degree of the engine in terms of cooling performance based on the cooling water temperature.

[0035] In one possible approach, the prediction unit further includes a third determining subunit and a fourth determining subunit. The third determining subunit is used to determine the engine's wear life based on the engine's various performance degradation degrees and its cumulative usage time, in response to the determination that all performance degradation degrees of the engine are less than the corresponding set performance degradation degree thresholds. The fourth determining subunit is used to determine the engine's remaining life based on its wear life and its design life.

[0036] In one possible approach, the prediction unit further includes a generation subunit. The generation subunit is configured to generate a maintenance suggestion based on the target performance degradation degree in response to determining that the target performance degradation degree is greater than or equal to a corresponding set performance degradation degree threshold, thereby prompting the engine to be repaired based on the maintenance suggestion; wherein the target performance degradation degree is any performance degradation degree in the engine.

[0037] According to a third aspect provided in this application, a vehicle is provided, which employs an engine life prediction device as described in the second aspect above.

[0038] According to a fourth aspect provided in this application, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement the method of the first aspect described above and any possible implementation thereof.

[0039] According to a fifth aspect provided in this application, a computer-readable storage medium is provided that, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the methods described in the first aspect and any possible implementation thereof.

[0040] According to the sixth aspect provided in this application, a computer program product is provided, the computer program product including computer instructions, which, when executed on an electronic device, cause the electronic device to perform the method described in the first aspect and any possible implementation thereof.

[0041] Therefore, the above-mentioned technical features of this application have the following beneficial effects:

[0042] (1) Operating parameters such as fuel economy, lubrication, and cooling are directly related to engine wear, aging, and failure mechanisms. Therefore, this application combines multiple operating parameters such as fuel economy, lubrication, and cooling to more accurately reflect the true deterioration state of the engine, thereby improving the accuracy of remaining life prediction. In addition, real-time monitoring of performance degradation can provide timely warnings of potential engine risks, enabling proactive maintenance to address these risks, reduce repair costs and downtime losses, and significantly reduce the probability of serious engine failures through proactive intervention, thus improving engine operational safety.

[0043] (2) The engine performance degradation is refined into core dimensions covering the combustion system, lubrication system and cooling system. By analyzing different performance degradation, the source of degradation can be accurately located, thereby improving the pertinence of engine fault diagnosis and providing a clear direction for maintenance.

[0044] (3) Since fuel consumption is greatly affected by operating conditions such as load, speed, and torque, directly using fuel consumption to judge the degree of performance degradation may lead to misjudgment. However, the fuel consumption rate can directly reflect the efficiency of the engine in converting the chemical energy of fuel into mechanical work. The first degree of performance degradation determined based on the fuel consumption rate can effectively eliminate the influence of operating condition fluctuations, accurately reflect the true degree of degradation of the engine in terms of fuel economy performance, and also provide a reliable basis for predicting the remaining life of the engine.

[0045] (4) Fuel consumption rate is the core indicator for measuring engine fuel economy. By analyzing the deviation between the current fuel consumption rate and the historical fuel consumption rate, the trend of fuel economy performance deterioration can be accurately judged, providing a basis for fault diagnosis and life prediction.

[0046] (5) Fuel rail pressure is a key parameter of the combustion system, directly determining the quality of fuel atomization. Therefore, this application can quickly assess the degree of degradation in engine fuel atomization performance by comparing the deviation between the current fuel rail pressure and various fuel rail pressures. Furthermore, by analyzing the degree of degradation, a basis can be provided for optimizing fuel atomization performance.

[0047] (6) Oil pressure is a core parameter of the lubrication system. A drop in oil pressure leads to poor lubrication, which in turn increases the risk of wear on various engine components. Therefore, by comparing the current oil pressure with the deviation of various historical oil pressures, the degree of deterioration in the engine's lubrication performance can be quickly assessed. In addition, by monitoring the degree of deterioration of this third performance in real time, early warnings can be issued, reducing maintenance costs and unexpected risks.

[0048] (7) The core function of the cooling system is to maintain the engine coolant temperature within the optimal range. Excessive coolant temperature leads to overheating, decreased mechanical strength, and lubrication failure; excessively low coolant temperature reduces thermal efficiency and increases fuel consumption. Therefore, by calculating the deviation between the current and historical coolant temperatures, the degree of engine cooling performance degradation can be reflected. Furthermore, real-time monitoring of this performance degradation can provide early warnings, reducing the risk of major malfunctions and maintenance costs.

[0049] (8) In this application, the fact that all performance degradation degrees of the engine are less than the corresponding set performance degradation degree thresholds indicates that the engine is in a controllable degradation stage with no major failure risk. In addition, each performance degradation degree directly quantifies the degradation degree of each core system of the engine. By comprehensively analyzing the degradation degree of multiple dimensions and the cumulative service time, the cumulative impact of the degradation of each system on the overall lifespan can be fully reflected, avoiding prediction deviations caused by the latent degradation of a certain system (such as abnormal intake manifold pressure), and making the lifespan prediction results closer to the actual state of the engine. Attached Figure Description

[0050] Figure 1 An architecture diagram of an engine life prediction system provided in this application embodiment;

[0051] Figure 2 An architecture diagram of another engine life prediction system provided in this application embodiment;

[0052] Figure 3 A flowchart illustrating an engine life prediction method provided in this application embodiment;

[0053] Figure 4 A schematic diagram illustrating the relationship between engine torque, power, mechanical losses, and rotational speed is provided for an embodiment of this application.

[0054] Figure 5 This application provides a schematic diagram illustrating the relationship between engine power and lifespan.

[0055] Figure 6 A flowchart illustrating another engine life prediction method provided in this application embodiment;

[0056] Figure 7 This is a schematic diagram of the structure of an engine life prediction device provided in an embodiment of this application;

[0057] Figure 8 This is a block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0058] To enable those skilled in the art to better understand the technical solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0059] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0060] In the embodiments of this application, the words "exemplary," "for example," or "for instance" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the words "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a specific manner.

[0061] First, the relevant technologies involved in this application will be explained to facilitate understanding by those skilled in the art.

[0062] As the core power source in automobiles and many other fields, the engine has a complex internal structure and diverse working environment. Therefore, accurately assessing the engine's lifespan is of great help in optimizing the engine's operation and formulating subsequent maintenance strategies, which can effectively improve the engine's reliability, economy, and safety.

[0063] Traditional engine life assessment primarily relies on inspecting key parameters such as the vehicle's fuel system, lubricating oil condition, and coolant temperature in a repair shop, combined with fault code reading and subjective noise assessment. However, this method has several significant drawbacks. From an efficiency perspective, the entire inspection and assessment process is time-consuming, requiring technicians to test and analyze each indicator individually, resulting in lengthy repair cycles and failing to meet users' demands for rapid service. In terms of cost, the need for specialized equipment and significant manpower keeps repair costs high, increasing the financial burden on users and limiting the overall efficiency improvement of the automotive aftermarket service industry.

[0064] More importantly, traditional assessment methods are essentially post-fault handling methods. They only address vehicle issues after obvious symptoms appear, failing to detect potential problems beforehand. During vehicle operation, sudden malfunctions are often unpredictable and can cause irreversible damage to hardware. For example, continuous operation of an engine under abnormal conditions such as high temperature or insufficient oil can lead to severe wear or even complete failure of critical components like pistons and cylinders. Furthermore, sudden malfunctions significantly increase the risk of traffic accidents, threatening the lives of passengers. Secondary damage after a malfunction is also extremely common; for instance, a collision can trigger a chain reaction of fuel leaks and short circuits, further exacerbating the damage.

[0065] To address the aforementioned technical problems, this application provides an engine life prediction method. By combining multi-dimensional operating parameters such as fuel economy, lubrication, and cooling, it can more accurately reflect the true deterioration state of the engine, thereby improving the accuracy of remaining life prediction. Furthermore, real-time monitoring of performance degradation can provide timely warnings of potential engine risks, enabling proactive maintenance to address these risks, reduce repair costs and downtime losses, and significantly reduce the probability of serious engine failures through proactive intervention, thus improving engine operational safety.

[0066] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0067] Figure 1 An architecture diagram of an engine life prediction system provided in this application embodiment is shown below. Figure 1 As shown, the system architecture includes: server 101, vehicle 102, and terminal device 103. The server 101, vehicle 102, and terminal device 103 are connected via communication links.

[0068] The server 101 can be a high-performance server providing various services on the internet, a standalone physical server, a server cluster consisting of multiple physical servers, or at least one of the following cloud servers providing basic cloud computing services: cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data or artificial intelligence platforms. This embodiment of the application does not limit this specific provision. Of course, the server can also include other functions to provide more comprehensive and diversified services.

[0069] The server 101 in this embodiment can be a single server, a server cluster, or a cloud server; this embodiment does not limit the specific server to this type.

[0070] Vehicle 102 can be a sedan, sport utility vehicle (SUV), truck, electric vehicle, motorcycle, tricycle, special vehicle (such as ambulance, fire truck, police car, etc.), driverless taxi, intelligent connected bus, autonomous logistics vehicle, electric truck, etc. Furthermore, this method is also applicable to various special-purpose vehicles, such as agricultural vehicles, mining vehicles, forestry vehicles, airport vehicles, port vehicles, etc. This application does not impose specific limitations on this. In the embodiments of this application, vehicle 102 can be referred to as a vehicle, mobile carrier, electric vehicle (EV), hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV), fuel cell vehicle (FCV), autonomous vehicle, intelligent and connected vehicle (ICV), driverless vehicle, etc., without limitation.

[0071] Terminal device 103 may be a device that provides voice and / or data connectivity to a user, a device with wireless connectivity, or other devices connected to a wireless modem. Terminal device 103 may be at least one of devices such as a mobile phone, desktop computer, laptop, wireless terminal, and laptop computer. In one embodiment, terminal device 103 has communication capabilities and can access wired or wireless networks.

[0072] This application embodiment does not limit the number of terminal devices 103 in the engine life prediction system, and may include more than... Figure 1 More or fewer terminal devices 103.

[0073] In this embodiment, vehicle 102 can send engine operating parameters (such as fuel economy parameters, lubrication parameters, cooling parameters, phaser response time, and intake manifold pressure) to server 101. Accordingly, server 101 can determine the engine's performance degradation degree based on the received engine operating parameters. Then, server 101 can predict the engine's remaining lifespan based on the performance degradation degree and generate an engine lifespan and health analysis report. This lifespan and health analysis report may include the engine's design life, remaining lifespan, health status, fault information, and maintenance recommendations. Finally, server 101 can send the lifespan and health analysis report to terminal device 103.

[0074] Optional, such as Figure 2 As shown, a vehicle may include an engine, an electronic control unit (ECU), an engine controller area network (CAN) bus, a gateway (GW), a vehicle central processing unit, a vehicle-side CAN bus, a microcontroller unit (MUC), and an onboard telematics box (TBOX).

[0075] See also Figure 2 The engine can send its operating parameters to the ECU. The ECU then sends these parameters, in a format defined by the CAN matrix, to the GW or vehicle central processing unit via the engine CAN bus. The GW or vehicle central processing unit can then send the operating parameters to the MUC via the vehicle's CAN bus. The MUC can then send the operating parameters to the TBOX via Ethernet or the vehicle's CAN bus. The TBOX can configure the operating parameters in the cloud and transmit the configured parameters to the server 101 via a cellular network or 4G network.

[0076] Accordingly, server 101 can decode, convert, configure, and store the received operating parameters, and then determine the engine's performance degradation level based on the processed operating parameters. Afterward, server 101 can predict the engine's remaining lifespan based on the performance degradation level, generate an engine lifespan and health analysis report, and send the report to terminal device 103 via a cellular network or 4G network, enabling the user to perform maintenance and support procedures on the engine based on the report.

[0077] For ease of understanding, the engine life prediction method provided in this application will be described in detail below with reference to the accompanying drawings.

[0078] Figure 3 This is a flowchart illustrating an engine life prediction method provided in an embodiment of this application, as shown below. Figure 3 As shown, the method includes:

[0079] S301. Determine the degree of performance degradation of the engine.

[0080] The engine performance degradation includes at least two of the following: the first degradation of the engine in terms of fuel economy, the second degradation of the engine in terms of fuel atomization, the third degradation of the engine in terms of cooling, the fourth degradation of the engine in terms of cooling, the fifth degradation of the engine in terms of control response, and the sixth degradation of the engine in terms of intake performance.

[0081] In some embodiments, the vehicle can acquire engine operating parameters in real time via sensors and then send these parameters to the server. Correspondingly, upon receiving the engine operating parameters, the server can decode, convert, and configure them, and store the processed parameters in a database. Subsequently, the server can determine the degree of engine performance degradation based on historical and current operating parameters.

[0082] The operating parameters may include at least one of the following: fuel economy parameters, lubrication parameters, cooling parameters, phaser response time, and intake manifold pressure. Fuel economy parameters may include fuel consumption, fuel consumption per 100 kilometers, and fuel rail pressure; lubrication parameters may include oil pressure, oil temperature, and oil viscosity; and cooling parameters may include coolant temperature, coolant flow rate, and fan control parameters. This application does not limit these parameters.

[0083] The first and second performance degradation degrees are determined based on the engine's fuel economy parameters, the third performance degradation degree is determined based on the engine's lubrication parameters, the fourth performance degradation degree is determined based on the engine's cooling parameters, the fifth performance degradation degree is determined based on the phaser response time, and the sixth performance degradation degree is determined based on the intake manifold pressure. The specific methods for determining each performance degradation degree can be found in the following embodiments and will not be elaborated upon here.

[0084] Optionally, operating parameters may also include engine torque, speed, power, etc., but this application does not limit this.

[0085] Optionally, the vehicle can also send its own driving parameters to the server. These driving parameters may include: vehicle speed, brake pedal opening, accelerator pedal opening, acceleration, deceleration, and steering wheel angle, etc., which are not limited in this application.

[0086] S302. Predict the remaining life of the engine based on the degree of performance degradation.

[0087] In some embodiments, the server can determine the engine's wear-out life based on the engine's wear-out life and cumulative usage time, in response to determining that all performance degradation levels of the engine are less than the corresponding set performance degradation thresholds. Then, the server can determine the engine's remaining life based on the wear-out life and the engine's design life. Finally, the server can generate a lifespan prediction report and send it to the terminal device, allowing the user holding the terminal device to understand the engine's current status based on the lifespan prediction report.

[0088] Optionally, the performance degradation thresholds set for each performance degradation level can be determined according to actual needs, such as 1, 2, etc., without limitation.

[0089] Specifically, the server is configured with a lifespan and health prediction model. The server can input at least two performance degradation levels of the engine, cumulative usage time, and design life into the health prediction model to obtain the engine's remaining lifespan output by the model. Among them, the at least two performance degradation levels can be any two, any three, any four, any five, or all performance degradation levels.

[0090] Taking the prediction of the engine's remaining lifespan based on all performance degradation levels (i.e., the first to the sixth performance degradation levels) as an example, the server can input the engine's various performance degradation levels, cumulative usage time, and design lifespan into the health prediction model to obtain the engine's remaining lifespan output by the model. For example, the lifespan and health prediction model satisfies the following formula 1.

[0091]

[0092] Where N represents the engine's remaining life in kilometers (km), S represents the engine's design life in kilometers (km), L represents the engine's cumulative service time in kilometers (km), α represents the first degree of performance degradation, β represents the second degree of performance degradation, γ represents the third degree of performance degradation, δ represents the fourth degree of performance degradation, and θ represents the fifth degree of performance degradation. This indicates the sixth performance degradation level.

[0093] Combining Formula 1 above, as the vehicle's mileage L increases, engine parts and the engine system will inevitably experience wear and tear and functional decline. When α, β, γ, δ, θ and As the value approaches zero, the engine's remaining lifespan also approaches its design lifespan, indicating that the engine is in a healthy state. When α, β, γ, δ, θ, and... As the value approaches 1, the engine's remaining lifespan approaches the difference between its design lifespan and its cumulative service life. When α, β, γ, δ, θ, and When all values ​​are greater than 1, it indicates that the engine is in a stage of excessive wear and aging, and its health condition is extremely poor.

[0094] In other embodiments, the server may generate maintenance recommendations based on the target performance degradation degree in response to determining that the target performance degradation degree is greater than or equal to the corresponding set performance degradation degree threshold, so as to prompt the engine to be repaired based on the maintenance recommendations.

[0095] The target performance degradation degree refers to any performance degradation degree in the engine.

[0096] Specifically, the server is configured with a fault diagnosis model. When any of the above-mentioned performance degradation levels is detected to be greater than or equal to the corresponding set performance degradation threshold, the server can input the operating parameters and the target performance degradation level into the fault diagnosis model to obtain maintenance suggestions output by the fault diagnosis model.

[0097] For example, assuming the fourth performance degradation degree δ is greater than the corresponding set performance degradation degree threshold (e.g., 1), the maintenance suggestions from the fault diagnosis model based on the operating parameters and the fourth performance degradation degree output may include: Fault cause: The cooling fan bearing is worn due to foreign objects entering it, increasing the bearing friction resistance and causing the fan speed to decrease linearly, while the fan current increases linearly. The fault may be due to the cooling fan being stuck. Maintenance suggestion: Repair or replace the cooling fan.

[0098] Based on the above technical solutions, and by combining multi-dimensional operating parameters such as fuel economy, lubrication, and cooling, the true deterioration state of the engine can be more accurately reflected, thereby improving the accuracy of remaining life prediction. Furthermore, real-time monitoring of performance degradation can provide timely warnings of potential engine risks, enabling proactive maintenance to address these risks, reduce repair costs and downtime losses, and significantly reduce the probability of serious engine failures through proactive intervention, thus improving engine operational safety.

[0099] In one optional implementation, the performance degradation degree may include a first performance degradation degree, a second performance degradation degree, a third performance degradation degree, a fourth performance degradation degree, a fifth performance degradation degree, and a sixth performance degradation degree. The following describes S302 in detail using the steps of 1. determining the first performance degradation degree, 2. determining the second performance degradation degree, 3. determining the third performance degradation degree, 4. determining the fourth performance degradation degree, 5. determining the fifth performance degradation degree, and 6. determining the sixth performance degradation degree as examples.

[0100] 1. Determine the first degree of performance degradation.

[0101] In some embodiments, the server can determine the engine's brake-specific fuel consumption (BSFC) based on the engine's torque, speed, and fuel consumption. Then, based on the BSFC, it can determine the engine's first degree of performance degradation in terms of fuel economy.

[0102] For example, the server can determine the engine's fuel consumption rate by referring to Formula 2 below.

[0103]

[0104] Among them, b e The values ​​represent the engine's fuel consumption rate, expressed in kilograms per kilowatt-hour (kg / kW·h). m represents fuel consumption (also known as fuel mass flow rate), expressed in kilograms per hour (kg / h). Te represents torque, expressed in Newton-meters (N·m). N represents engine speed, expressed in revolutions per minute (rpm).

[0105] In other embodiments, the server can determine the engine's fuel consumption rate based on vehicle speed, fuel density, fuel consumption per 100 kilometers, and engine output power. Then, based on the fuel consumption rate, it can determine the engine's first degree of performance degradation in terms of fuel economy.

[0106] For example, the server can determine the engine's fuel consumption rate by referring to Formula 3 below.

[0107]

[0108] Wherein, FC represents fuel consumption per 100 kilometers, measured in liters per 100 kilometers (L / 100km). P represents engine output power, measured in kilowatts (kW). ρ represents fuel density, measured in kilograms per liter (kg / L). v represents vehicle speed, measured in kilometers per hour (km / h).

[0109] In summary, such as Figure 4 As shown in (b), the torque initially increases with increasing speed, but after reaching a certain threshold, it decreases with further increases in speed; as... Figure 4 As shown in (c), the power increases with increasing rotational speed; as Figure 4 As shown in (a), the mechanical losses of the engine also increase with increasing engine speed, thus affecting engine life. Figure 5As shown, engine lifespan and power are negatively correlated. For example, when the power is Ps (i.e., high speed and heavy load), the engine lifespan Hs is the shortest; when the power is the rated power Pe, the engine lifespan He is in the middle; and when the power is Pm (i.e., low speed and light load), the engine lifespan Hm is the longest. In summary, engine lifespan is strongly correlated with power, speed, and torque. Therefore, the fuel consumption rate determined based on information such as power, speed, and torque can not only measure the engine's fuel economy performance but also its lifespan and health; that is, the lower the fuel consumption rate, the better the engine's lifespan and health.

[0110] In some embodiments, the server can determine the engine's historical fuel consumption rates and current fuel consumption rates in the manner described above. Then, the server can determine the differences between each historical fuel consumption rate and the current fuel consumption rate, and then determine the ratio between each difference and the current fuel consumption rate. Finally, the server can determine the average of these ratios as the engine's first degree of performance degradation in terms of fuel economy.

[0111] The aforementioned historical fuel consumption rates are determined based on the number of driving cycles of the vehicle. For example, the period from the first power-on to the first power-off constitutes one driving cycle, the period from the second power-on to the second power-off constitutes two driving cycles, and so on.

[0112] For example, the server can refer to Formula 4 below to determine the first performance degradation degree.

[0113]

[0114] Where n represents the number of driving cycles, be_i represents the fuel consumption rate of the i-th driving cycle, and be_m represents the current fuel consumption rate.

[0115] Based on the above, if α is less than or equal to zero, it indicates that the engine's current fuel consumption rate has increased, and the engine performance and wear level have deteriorated compared to the initial state. If α is greater than zero and less than 1, it indicates that the engine's current fuel consumption rate has decreased, the engine is in a good break-in period, and the engine is in excellent health. If α is greater than or equal to 1, the server determines it to be an abnormal state, requiring fault analysis.

[0116] 2. Determine the second degree of performance degradation.

[0117] In some embodiments, the server can refer to the method described above for determining the first performance degradation degree to determine each difference between the historical fuel rail pressure and the current fuel rail pressure, and then determine each ratio between each difference and the current fuel rail pressure. Finally, the server can determine the average of the ratios as the second performance degradation degree of the engine in terms of fuel atomization performance.

[0118] For example, the server can refer to Formula 5 below to determine the second performance degradation degree.

[0119]

[0120] Where Pfuel_i represents the fuel rail pressure in the i-th driving cycle, and Pfuel_m represents the current fuel rail pressure.

[0121] In the engine fuel system, when the fuel pump, high-pressure fuel pump and fuel lines malfunction, the main symptoms are insufficient fuel supply, internal fuel leakage, and deterioration of fuel atomization performance. All of these faults will be reflected in the fuel rail pressure Pfuel parameter. Therefore, by comparing the current fuel rail pressure with the historical fuel rail pressure, the life and health of the engine fuel system can be assessed.

[0122] 3. Determine the degree of third-party performance degradation

[0123] In some embodiments, the server can refer to the method described above for determining the first performance degradation degree to determine each difference between historical oil pressure and current oil pressure, and then determine each ratio between each difference and current oil pressure. Finally, the server can determine the average of each ratio as the second performance degradation degree of the engine in terms of lubrication performance.

[0124] For example, the server can refer to Formula 6 below to determine the third performance degradation degree.

[0125]

[0126] Where Poil_i represents the oil pressure during the i-th driving cycle, and Poil_m represents the current oil pressure.

[0127] Oil pressure is a core parameter of the engine lubrication system, affecting the oil pump and the sealing performance of the lubrication circuit. When the oil pressure is normal, a stable oil film forms, ensuring adequate lubrication of all friction pairs (crankshaft bearings, camshaft, etc.). When the oil pressure is too low, the oil film ruptures, leading to abnormal wear of the camshaft or seizing of the turbocharger bearings. When the oil pressure is too high, the oil pump overloads, oil circuit seals (such as oil seals and gaskets) are compressed, or the oil pump's power consumption increases. Therefore, comparing the current oil pressure with historical oil pressures can be used to assess the lifespan and health of the engine lubrication system.

[0128] 4. Determine the fourth performance degradation degree.

[0129] In some embodiments, the server can refer to the method described above for determining the first performance degradation degree to determine each difference between each historical coolant temperature and the current coolant temperature, and then determine each ratio between each difference and the current coolant temperature. Finally, the server can determine the average of the ratios as the fourth performance degradation degree of the engine in terms of cooling performance.

[0130] For example, the server can refer to Formula 7 below to determine the fourth performance degradation degree.

[0131]

[0132] Where Twater_i represents the coolant temperature during the i-th driving cycle, and Twater_m represents the current coolant temperature.

[0133] Coolant temperature is a core parameter of the engine cooling system. When critical components of the cooling system, such as the water pump and thermostat, deteriorate or fail due to prolonged use, the engine coolant temperature will rise significantly, exhibiting the following characteristics: (a) Decreased cooling capacity: The coolant temperature struggles to maintain a stable equilibrium or takes longer to reach equilibrium, and the equilibrium temperature is higher than normal, directly reflecting the health status of the cooling system components. (b) Torque limiting protection trigger: If Twater ≥ M℃ (usually M = 105℃, the specific threshold varies depending on the system), the engine activates a torque limiting control strategy, reducing output power to force cooling. (c) Fuel cut-off and shutdown protection: When Twater further rises to N℃ (usually N = 110℃, the specific threshold varies depending on the system) or exceeds N℃, the engine triggers the highest level of protection logic, directly cutting off fuel and shutting off the engine to avoid mechanical damage caused by high temperatures (such as cylinder scoring, cylinder head deformation, etc.). Therefore, comparing the current coolant temperature with historical coolant temperatures can be used to assess the lifespan and health of the engine cooling system.

[0134] 5. Determine the fifth performance degradation level.

[0135] In some embodiments, the server can refer to the method described above for determining the first performance degradation degree to determine each difference between the response time of each historical phaser and the current phaser response time, and then determine each ratio between each difference and the current phaser response time. Finally, the server can determine the average of the ratios as the fifth performance degradation degree of the engine in terms of control response performance.

[0136] For example, the server can refer to Formula 8 below to determine the fifth performance degradation degree.

[0137]

[0138] Where Tvvt_i represents the phaser response time of the i-th driving cycle, and Tvvt_m represents the current phaser response time.

[0139] As a core component of the variable valve timing system, the phaser's main function is to dynamically adjust the relative phase angle between the camshaft and crankshaft according to commands from the electronic control unit, thereby optimizing valve opening and closing timing. The phaser's response time is related to oil pressure. Insufficient oil pressure may cause solenoid valve blockage, leading to a phaser response delay. This delay can cause uncontrolled valve overlap, significantly reducing the system's dynamic adjustment capability and ultimately affecting key performance aspects such as engine torque output, fuel economy, and emissions control.

[0140] 6. Determine the sixth performance degradation degree.

[0141] In some embodiments, the server can refer to the method described above for determining the first performance degradation degree to determine each difference between the historical intake manifold pressure and the current intake manifold pressure, and then determine each ratio between each difference and the current intake manifold pressure. Finally, the server can determine the average of the ratios as the second performance degradation degree of the engine in terms of intake performance.

[0142] For example, the server can refer to Formula 9 below to determine the sixth performance degradation degree.

[0143]

[0144] Where Pair_i represents the intake manifold pressure of the i-th driving cycle, and Pair_m represents the current phaser response time.

[0145] Changes in intake manifold pressure can reflect combustion chamber sealing, piston assembly conditions, and turbocharger system performance. When piston rings and cylinder bores wear excessively, combustion chamber pressure decreases. Since combustion chamber pressure and intake manifold pressure are linearly correlated, the intake manifold pressure will decrease synchronously. For turbocharged engines, if the turbine or compressor performance deteriorates, its boost efficiency will decrease, also leading to a significant drop in intake manifold pressure. By monitoring abnormal changes in intake manifold pressure, fault trends such as piston assembly seal failure and turbocharger system functional degradation can be effectively diagnosed.

[0146] The engine life prediction method provided in this application will be described in detail below with reference to the above embodiments. Figure 6 A flowchart illustrating another engine life prediction method provided in this application embodiment is shown below. Figure 6 As shown, the method includes:

[0147] S601. Obtain engine operating parameters and fault status information.

[0148] Among them, the fault status information is used to indicate whether the engine has malfunctioned.

[0149] S602. Determine whether the fault status information indicates an engine malfunction. If not, proceed to S603. If yes, proceed to S607.

[0150] S603. Based on operating parameters, determine the degree of degradation of various engine performance characteristics.

[0151] S604. Determine whether each performance degradation degree is less than the corresponding set performance degradation degree threshold. If yes, execute S605; otherwise, execute S606.

[0152] S605. Input the engine's various performance degradation levels, cumulative service time, and design life into the life and health prediction model to obtain the engine's predicted remaining life.

[0153] S606. Input the operating parameters and various performance degradation levels into the fault diagnosis model to obtain the maintenance suggestions output by the model.

[0154] S607. Input the operating parameters into the fault diagnosis model to obtain the fault information output by the model, so as to prompt the user to carry out maintenance and troubleshooting based on the fault information.

[0155] In some embodiments, the server can send fault information to the terminal device, enabling the user to troubleshoot and repair the fault based on the fault information. Additionally, the server can send fault information to the vehicle; upon receiving the fault information, the vehicle can activate the malfunction indicator lamp (MIL) via the controller, putting the vehicle into a repair-ready mode.

[0156] Figure 7 This is a schematic diagram of the structure of an engine life prediction device provided in an embodiment of this application, as shown below. Figure 7 As shown, the engine life prediction device includes a determination unit 701 and a prediction unit 702.

[0157] The determining unit 701 is used to determine the performance degradation degree of the engine; the performance degradation degree includes at least two of the following: a first performance degradation degree in fuel economy performance, a second performance degradation degree in fuel atomization performance, a third performance degradation degree in lubrication performance, and a fourth performance degradation degree in cooling performance; the first and second performance degradation degrees are determined based on the engine's fuel economy parameters, the third performance degradation degree is determined based on the engine's lubrication parameters, and the fourth performance degradation degree is determined based on the engine's cooling parameters.

[0158] Prediction unit 702 is used to predict the remaining life of the engine based on the degree of performance degradation.

[0159] In one possible embodiment, the determining unit 701 further includes a first determining subunit and a second determining subunit. The first determining subunit is used to determine the engine's fuel consumption rate based on the engine's torque, speed, and fuel consumption. The second determining subunit is used to determine a first degree of performance degradation in the engine's fuel economy based on the fuel consumption rate.

[0160] In one possible approach, the first determining subunit is further configured to determine the engine's fuel consumption rate based on vehicle speed, fuel density, fuel consumption per 100 kilometers, and engine output power. The second determining subunit is further configured to determine a first degree of performance degradation in the engine's fuel economy based on the fuel consumption rate.

[0161] In one possible approach, the second determining subunit is specifically used to determine each difference between each historical fuel consumption rate and the fuel consumption rate, then determine each ratio between each difference and the fuel consumption rate, and determine the average of each ratio as the first performance degradation degree of the engine in terms of fuel economy.

[0162] In one possible approach, the determining unit 701 is specifically used to determine a second performance degradation degree of the engine in terms of fuel atomization performance based on the fuel rail pressure.

[0163] In one possible approach, the determining unit 701 is further used to determine the third performance degradation degree of the engine in terms of lubrication performance based on the oil pressure.

[0164] In one possible approach, the determining unit 701 is further used to determine a fourth performance degradation degree of the engine in terms of cooling performance based on the cooling water temperature.

[0165] In one possible embodiment, the prediction unit 702 further includes a third determining subunit and a fourth determining subunit. The third determining subunit is used to determine the engine's wear life based on the engine's various performance degradation degrees and the engine's cumulative usage time, in response to determining that all performance degradation degrees of the engine are less than the corresponding set performance degradation degree thresholds. The fourth determining subunit is used to determine the engine's remaining life based on the wear life and the engine's design life.

[0166] In one possible embodiment, the prediction unit 702 further includes a generation subunit. The generation subunit is configured to generate a maintenance suggestion based on the target performance degradation degree in response to determining that the target performance degradation degree is greater than or equal to a correspondingly set performance degradation degree threshold, thereby prompting the engine to be repaired based on the maintenance suggestion; wherein the target performance degradation degree is any performance degradation degree in the engine.

[0167] Figure 8 This is a block diagram of an electronic device provided in an embodiment of this application. (For example...) Figure 8As shown, the electronic device includes, but is not limited to, a processor 801 and a memory 802.

[0168] The memory 802 described above is used to store the executable instructions of the processor 801. It is understood that the processor 801 is configured to execute instructions to implement the engine life prediction method in the above embodiments.

[0169] It should be noted that those skilled in the art will understand that Figure 8 The electronic device structure shown does not constitute a limitation on the electronic device; the electronic device may include, but is not limited to, other electronic devices. Figure 8 This may indicate more or fewer components, or combinations of certain components, or different component arrangements.

[0170] The processor 801 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 802, and by calling data stored in the memory 802, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. The processor 801 may include one or more processing units. Optionally, the processor 801 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 801.

[0171] The memory 802 can be used to store software programs and various data. The memory 802 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required by at least one functional module (such as a determination unit, processing unit, etc.), etc. Furthermore, the memory 802 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0172] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 802 including instructions, which can be executed by a processor 801 of an electronic device to implement the methods in the above embodiments.

[0173] In actual implementation, Figure 7 The functions of both the determination unit 701 and the prediction unit 702 can be determined by... Figure 8 The processor 801 calls the computer program stored in the memory 802 to implement the process. The specific execution process can be found in the method section of the previous embodiment, and will not be repeated here.

[0174] Optionally, the computer-readable storage medium may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0175] In an exemplary embodiment, this application also provides a computer program product including one or more instructions, which can be executed by a processor 801 of an electronic device to perform the methods described above.

[0176] It should be noted that when one or more instructions in the computer-readable storage medium or computer program product are executed by the processor of an electronic device, they implement the various processes of the above method embodiments and achieve the same technical effect as the above method. To avoid repetition, they will not be described again here.

[0177] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0178] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0179] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0180] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0181] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0182] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for predicting engine life, characterized in that, The method includes: Determine the degree of performance degradation of the engine; the degree of performance degradation includes at least two of the following: a first degree of performance degradation in fuel economy, a second degree of performance degradation in fuel atomization, a third degree of performance degradation in lubrication, and a fourth degree of performance degradation in cooling; the first degree of performance degradation and the second degree of performance degradation are determined based on the engine's fuel economy parameters, the third degree of performance degradation is determined based on the engine's lubrication parameters, and the fourth degree of performance degradation is determined based on the engine's cooling parameters; Based on the degree of performance degradation, the remaining life of the engine is predicted.

2. The engine life prediction method according to claim 1, characterized in that, The fuel economy parameters include fuel consumption; determining the engine performance degradation includes: The fuel consumption rate of the engine is determined based on the engine's torque, speed, and fuel consumption. Based on the fuel consumption rate, the first performance degradation degree of the engine in terms of fuel economy is determined.

3. The engine life prediction method according to claim 1, characterized in that, The fuel economy parameters include fuel consumption per 100 kilometers, which refers to the amount of fuel consumed by the vehicle when driving 100 kilometers; determining the engine performance degradation includes: The fuel consumption rate of the engine is determined based on vehicle speed, fuel density, fuel consumption per 100 kilometers, and engine output power. Based on the fuel consumption rate, the first performance degradation degree of the engine in terms of fuel economy is determined.

4. The engine life prediction method according to claim 2 or 3, characterized in that, The determination of the first performance degradation degree of the engine in terms of fuel economy based on the fuel consumption rate includes: Determine the differences between each historical fuel consumption rate and the stated fuel consumption rate; Determine the ratio between each of the aforementioned differences and the fuel consumption rate; The average of the various ratios is determined as the first degree of performance degradation of the engine in terms of fuel economy.

5. The engine life prediction method according to claim 1, characterized in that, The fuel economy parameters include fuel rail pressure; determining the degree of engine performance degradation includes: Based on the fuel rail pressure, a second performance degradation degree in the engine's fuel atomization performance is determined.

6. The engine life prediction method according to claim 1, characterized in that, The lubricity parameters include oil pressure; determining the degree of engine performance degradation includes: Based on the oil pressure, the third performance degradation degree of the engine in terms of lubrication performance is determined.

7. The engine life prediction method according to claim 1, characterized in that, The cooling parameters include coolant temperature; determining the degree of engine performance degradation includes: Based on the cooling water temperature, the fourth performance degradation degree of the engine in terms of cooling performance is determined.

8. The engine life prediction method according to claim 1, characterized in that, The performance degradation also includes: The fifth performance degradation degree of the engine in terms of control response performance, determined based on the phaser response time; The sixth performance degradation degree of the engine in terms of intake performance is determined based on the intake manifold pressure.

9. The engine life prediction method according to any one of claims 1-3 or 5-8, characterized in that, The method of predicting the remaining life of the engine based on the degree of performance degradation includes: In response to determining that each performance degradation degree of the engine is less than the corresponding set performance degradation degree threshold, the wear life of the engine is determined based on each performance degradation degree of the engine and the cumulative usage time of the engine. The remaining life of the engine is determined based on the wear life and the design life of the engine.

10. The engine life prediction method according to claim 9, characterized in that, The method further includes: In response to determining that the target performance degradation degree is greater than or equal to the corresponding set performance degradation degree threshold, a maintenance suggestion is generated based on the target performance degradation degree to prompt the engine to be repaired based on the maintenance suggestion; The target performance degradation degree can be any performance degradation degree in the engine.

11. An engine life prediction device, characterized in that, The device includes: A determining unit is used to determine the performance degradation degree of an engine; the performance degradation degree includes at least two of the following: a first performance degradation degree in fuel economy, a second performance degradation degree in fuel atomization, a third performance degradation degree in lubrication, and a fourth performance degradation degree in cooling; the first and second performance degradation degrees are determined based on the engine's fuel economy parameters, the third performance degradation degree is determined based on the engine's lubrication parameters, and the fourth performance degradation degree is determined based on the engine's cooling parameters; A prediction unit is used to predict the remaining life of the engine based on the degree of performance degradation.

12. A vehicle, characterized in that, The vehicle is equipped with the engine life prediction device as described in claim 11.

13. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the engine life prediction method as described in any one of claims 1-10.

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