Prognostics and Health Management System for Electric Vehicles with a Hierarchy Fusion Framework: Concepts, Architectures, and Methods

*e prognostics and health management (PHM) of electric vehicles is an important guarantee for their safety and long-term development. At present, there are few studies researching about life cycle PHM system of electric vehicles. In this paper, we first summarize the research progress and key methods of PHM. *en, we propose a three-level PHM system with a hierarchy fusion architecture for electric vehicles based on the structure, data source of them. In the PHM system, we introduce a database consisting of the factory data, real-time data, and detection data.*e electric vehicle’s factory parameters are used for determining the life curve of the electric vehicle and its components, the real-time data are used for predicting the remaining useful lifetime (RUL) of the electric vehicle and its components, and the detection data are used for fault diagnosis. *is health management database is established to help make condition-based maintenance decisions for electric vehicles. In this way, a complete electric vehicle PHM system is formed, which can realize the whole-life-cycle life prediction and fault diagnosis of electric vehicles.


Introduction
e development of new-energy-related technologies and the advancement of environmental awareness have promoted the rise of new energy vehicle [1,2], and electric vehicle is one of the important branches. Electric vehicles have become the development trend in the automotive field since their energy-saving nature.
Compared with traditional automobiles, electric vehicles are different in electric drive and control systems due to different energy consumption types. Meanwhile, the fault and accident types of electric vehicles are also different from traditional vehicles. In terms of the fault type, electric vehicles may fail in the battery system (such as inconsistency of cells and degradation of battery), while traditional vehicles may fail in their fuel engine (such as the failure of the cooling system). In terms of the accident type, fire accidents are more likely to occur in electric vehicles than traditional ones. In recent years, many electric vehicle accidents have occurred in different countries. Take China as an example; 59 accidents such as fire accidents occurred in electric vehicles in China between 2016 and 2018. e causes of the accidents are related to many factors of the battery system such as battery aging and overcharging. e health management mode of traditional fuel vehicles cannot be directly used. erefore, it is urgent to establish a prognostics and health management (PHM) system specifically for the essential characteristics of electric vehicles, comprehensively monitor the health state of electric vehicles, and improve the safety level of electric vehicles. e contribution of this study is that we show a comprehensive PHM system for electric vehicles.
is paper summarizes the research progress of health management and the demand of electric vehicle health management and then proposes a comprehensive three-level PHM system with a hierarchy fusion architecture for electric vehicles. e remainder of this paper is organized as follows. Section 2 introduces the content of PHM. Section 3 describes the architectures and key methods of the PHM method. Section 4 proposes a three-level PHM system with a hierarchy fusion architecture for electric vehicles. Finally, we conclude the work of this paper.

What Is PHM?
e content of PHM includes two aspects, namely, prognostics and health management. Prognostics is to predict the future status based on the current and historical status of the system, including the health state, remaining useful lifetime, and faults of the system and components. Health management is to make decisions on maintenance based on fault prediction information, maintenance resources, and application requirements. Prognostics is the core aspect of health management. PHM systems usually have functions such as fault detection, fault isolation, fault diagnosis, fault prediction, health management, and life tracking. For complex equipment and systems, PHM can achieve comprehensive diagnosis and fault prediction at different levels.
In the 20th century, the US military first proposed the concept of health management. Health management aims to monitor equipment working conditions, predict its remaining useful lifetime, potential failures, and health issues, and provide repair and maintenance recommendations. Advanced sensor technology, data transmission technology, and data processing technology are often used as the technical methods. In recent years, health management technology has received extensive attention, research, and application and has now developed into prognostics and health management technology [3][4][5]. Lee [6] proposed a "5S"-based health management system and process for the mechanical system. Brahimi et al. [7] summarized the health management system of railway infrastructure. Pecht [8] analyzed the health management and technical methods of electronic systems. Johnson et al. [9] described the health management system and application of the space system. In addition to military field, health management technology plays an important role in the mechanical system [6,10], battery health management system [11,12], aerospace [13], and other fields. For example, the PHM technology is widely used in the aircraft system in the UK, the US, and Canada, as well as other complex engineering equipment such as automobiles. Vehicle health management (VHM) system has been utilized to realize the automated decisions in the last decades. For instance, the OnStar system of General Motors company sets a good example for the use of VHM. is system is capable of prognostics and information transmission from subsystem to a central processing system. is centralized architecture and data processing technology can be applied to the electric vehicle PHM system design.

Why PHM for Electric Vehicles?
e market of electric vehicles has been expanding a lot for the last decade and the number of electric vehicles is increasing sharply. e monitoring of the safety level and identifying of possible faults about the vehicle in use are of great significance. Electric vehicle is a complex system composed of the battery system, motor system, and electronic control system, and the safety performance is closely related to each system. However, nowadays the health management is mainly concentrated on battery systems [14][15][16]. A mature electric vehicle health management system framework has not yet been formed. erefore, it is necessary to establish a health management system for electric vehicles, which will directly affect the long-term development of electric vehicles. e PHM system has been successfully applied to some complex system such as the aircraft system. And, it has advantages over the whole-life cycle health management. erefore, introducing the idea of PHM to the electric vehicle field is a promising idea.

PHM Architectures and Key Methods
In this section, we will give an overview about the PHM architectures, especially three different classifications. Next, two important technologies used in the PHM system will be introduced, namely, the life prediction and fault diagnosis. Fault diagnosis is used to realize the prognosis, and life prediction is an import branch of health management. us, we mainly carry on the elaboration in these two aspects.
3.1. PHM Architectures. PHM architectures are divided into three categories: centralized architecture, distributed architecture, and hierarchy fusion architecture. In the centralized PHM architecture, the information is processed within a central management controller which integrates the function of information collection, transformation, and processing. In a distributed architecture, each subsystem can realize the information processing process and then achieve the function of fault diagnosis and prediction. After the processing of the subsystem, the health status of the subsystem can be directly transmitted to the integrated display and control unit. e hierarchical fusion architecture combines the centralized and distributed architecture. Each subsystem has basic PHM functions, while the higher-level system has more powerful integration capability. And the high-level system can comprehensively process and control all diagnosis and prediction results.

Life Prediction Methods.
ere exist three kinds of methods to make the life prediction: physical model-based method, data-driven method, and fusion method. e physical model-based method establishes a physical failure model to investigate the internal cause of the system failure and then predicts the remaining useful lifetime (RUL) of the system [17]. Son et al. [18] proposed a RUL prediction method based on the gamma process. A nonstationary gamma process model was used to establish a system degradation model to predict the RUL. Wang [19] proposed a second-order remaining life prediction model based on semirandom, semilinear, and non-Gaussian filtering techniques based on degradation data. Lin [20] studied the two-stage modelling method of failure based on Bayesian change point estimation, mainly considering the existence of change points in the system failure rate function. Yuan and Yuo [21] also considered the existence of change points in the failure rate function. e physical model-based method can accurately predict the remaining life of the equipment, but it is often difficult to obtain the model parameters. Meanwhile, it is difficult to establish a physical model for the failure of complex systems.
Data-driven methods can overcome those aforementioned difficulties. ese kinds of methods use system detection data to establish the state models of the characteristic index and the remaining life of the system. Data-driven methods include neural networks, support vector machines, Bayesian networks, and stochastic processes. Wang and Vachtsevanos [22] realized the life prediction of the automobile-bearing system through the dynamic wavelet neural network model. You et al. [23] proposed a method for predicting the real-time state of electric vehicle power batteries based on neural network models and proved the accuracy of the method through battery tests. Khelif et al. [24] used support vector machines to build a model between the remaining life and sensor parameters. In addition, according to different degradation models, researchers have also established RUL prediction methods based on the Gamma process [25] and Wiener process [26,27]. Datadriven methods require large-scale and high-quality datasets. At the same time, the algorithm needs to make a balance between the accuracy and complexity so that the efficiency of the algorithm is acceptable. e fusion method combines the advantages of the physical model-based method and the data-driven method. Liao Kottig [28] proposed a hybrid method combining a data-driven method and a physical model method. e validity of the method was proved by the prediction of the remaining life of the lithium battery. e fusion method can combine the advantages of the first two methods, but the algorithm is more complicated.

Fault Diagnosis Methods.
A fault is a deviation from expectations of the parameters or states in a normally operating system. Fault diagnosis technologies mainly include model-based fault diagnosis and data-based fault diagnosis. e model-based method compares the model output information with the actual monitoring information of the system to determine whether the system has failed. Common methods include observer-based methods [29,30] and parameter identification-based methods [31]. Observer-based methods are flexible and easy to apply, such as Kalman filter (EKF) [32], adaptive EKF (AEKF) [33], sliding model observer (SMO) [34], or proportional integral observer (PIO) [30]. e parameter identification-based methods detect abnormalities through online parameter estimation to diagnose faults. However, the application of model-based method is limited since it is difficult to obtain an accurate system model for a complex system. e data-based method analyzes and diagnoses based on a large amount of historical data without the need for accurate system models and signal types. e main methods include neural networks, decision trees, and support vector machines. Yam realized the prediction of system failure through the regression neural network model [35]. Wang chose a fuzzy neural network model to predict and determine system failures [36]. Qiu et al. proposed a fault recognition algorithm based on Hidden Markov for fault sign recognition at an early stage [37]. Goebel compared the performance of three data analysis methods, i.e., neural network, decision tree, and support vector machine in fault prediction [38]. Skormi et al. realized prediction of the fault development process based on clustering algorithm [39]. e rise of electric vehicles in recent years has accumulated a small volume of fault data. And, the methods based on big data are not applicable due to the small-scale sample data. ere are some studies dealing with the fault diagnosis of small-scale data. Vachtsevanos [40] calculated the probability density of fault by statistical methods such as Bayes. Weibull [41] used the Weibull distribution to expand the sample data. e application of Monte Carlo and Bayesian methods [42] to obtain the probability density of faults is often used.
In summary, the PHM method is mainly oriented to fault prediction and fault diagnosis.
ere are some differences in the methods and application requirements in different fields. For the PHM for electric vehicles, we should not only focus on battery system health management but also establish a complete PHM system for the entire vehicle.

PHM System for Electric Vehicles
4.1. PHM System. PHM system for electric vehicle mainly concerns with the following two problems: prognostics and health management. First health management deals with the life prediction and evaluation for electric vehicle and its components. And, prognostics deals with the fault diagnosis and prediction. ree kinds of PHM architectures are described before, and now we will introduce a three-level PHM system with a hierarchy fusion architecture for electric vehicles considering the vehicle structure, data source, and health management requirements of them. e PHM system is shown in Figure 1. e detailed composition of the database is shown in Figure 2. ere are four sources of data in this database, namely, the electronic control system, motor system, battery system, and electric vehicle. For each component, factory data, detection data, and real-time data are included to provide a comprehensive view about the vehicle. According to different stages of data source in the health management database, the PHM system is divided into three levels: Level 1 deals with the life prediction of electric vehicles at the factory stage. In this level, the lifetime of electric vehicles and its subsystems and components are predicted. e main product is the life curves of electric vehicle, battery system, motor system, electronic control system, and components.

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Level 2 deals with the real-time remaining useful lifetime prediction of electric vehicles. In this level, the remaining useful lifetime is predicted through the real-time operation data of electric vehicle to realize the real-time monitoring and evaluation of electric vehicle's state.
Electric vehicle Electronic control system Motor system Battery system

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Level 3 deals with the fault diagnosis of electric vehicle detection process. In this level, we detect the faults of the electric vehicle and diagnose the faulty system or components.
e main functions of the electric vehicle PHM system include information collection, fault detection, fault diagnosis, fault prediction, and health management. e details of each function are listed as follows.
(1) Information collection: integrate the factory parameters, test data, and real-time driving data of electric vehicles to form a health management database (2) Fault detection: determine the performance degradation state of battery, motor, and electronic control system, such as normal state, performance degradation state, or a function failure state (3) Fault diagnosis: determine the fault system or parts to be repaired (4) Fault prediction: predict the time when the battery, motor, and electronic control system will fail (5) Health management: make condition-based maintenance decision on battery, motor, electronic control system, and parts according to the information of fault detection, fault diagnosis, and fault prediction Next, we will introduce the key methods of the PHM system for electric vehicles. Firstly, we will elaborate the life prediction at factory stage and real-time life prediction of electric vehicle in use (postfactory stage). e life prediction at the factory stage is a necessary input for the real-time life prediction, which provides information for health management. Secondly, we will show the fault diagnosis of electric vehicle.

Life Prediction of Electric Vehicles at
Factory Stage e key components of a vehicle will have permanent damage during long-time driving, and its fatigue life has always been the focus of vehicle safety. e life of the components of electric vehicles and the life of battery, motor, and electronic control systems are closely related. For example, the individual battery cells in the battery system constitute the entire battery pack. And the battery system, the motor system, and the electronic control system interact with each other to form the entire vehicle. us, the life of electric vehicle is closely related to its components and subsystems. rough the method of nonlinear and linear fusion, the life curve can be obtained from the component to the entire vehicle. erefore, the life prediction of the electric vehicle at the factory stage includes the life curve of single components, the life curve of the subsystem, and the life curve of the entire vehicle.

Life Curve of Single Component.
e life of electric vehicle components changes with time and shows nonlinear characteristics. So, the traditional regression algorithm is difficult to predict the life accurately. Taking the battery system as an example, its life prediction methods mainly includes physical model-based method, data-driven method, and fusion method. And, the algorithm based on data-driven has good adaptability, among which the BP neural network is used the most widely. rough the error backpropagation algorithm, the model parameters can be self-updated and the training data can be fitted. e model structure is shown in Figure 3.
Here, we take the battery system as an example. Assume that the parameters of the component are the number of charge and discharge, discharge voltage, and temperature, which is noted as vector [x 1 , x 2 , x 3 ]. Using this vector as the input data of the neural network, through multiple hidden layers and delinearized softmax layers, the feature of the part can be extracted and the corresponding component life can be predicted. e model can also be applied to life prediction of other components such as motor bearings. By collecting the vibration data of the motor bearing, transforming it to the frequency domain for feature analysis, and extracting indicators related to bearing performance degradation through PCA (Principal Component Analysis), we can obtain the input data. Taking these indicators as the input and the predicted life of the motor bearing as the output, the model is established.

Life Curve of Subsystems Based on Linear Fusion.
e life of each component is given in different weights according to its importance. e system life prediction model can be obtained by the linear weighting method : where T represent the life of system, n indicates the number of components that make up the system, t i is the life of component i, and ω i is the weight of component i. is method can be applied to the life prediction of the battery, motor, and electronic control system. Taking battery pack as an example, the life of single battery is predicted by particle filter and data-driven algorithm.
en, the life of the whole battery pack is obtained by weighting the life of all single batteries, in which the weight of each single battery is the same.
Advances in Civil Engineering e linear weighting method is simple to realize, but the results of prediction is not good. Due to the nonlinear characteristics of electrical components and complex driving conditions, the linear model with the same weight assigned to subsystem cannot be directly applied to different conditions. Aiming at this problem, the weight is adjusted dynamically to improve the applicability of the model.

Life Curve of Systems Based on Nonlinear Fusion.
e commonly used methods are the waterfall fusion method and feature fusion method. e waterfall fusion method is suitable for modules with strong correlation between various components.
is method connects each component in series.
e life of the previous component will affect the next component. It is progressively connected in series to finally obtain the life of the entire module. e feature fusion method is a statistical-based method. It collects the life curve and parameters of the same module in multiple samples and digs the statistical rules to obtain the life curve of this module.
is method can be applied to the life prediction of batteries, motors, and electronic control systems. Take the motor system as an example. e system includes bearings, insulation, DC bus capacitors, IGBT (insulated gate bipolar transistor), and other electronic components. e series model is shown in Figure 4. e life of the whole system is composed of all components in series, not a simple linear combination.

Life Curve of the Entire Electric Vehicle.
e relationship between the battery, motor, and electronic control systems of an electric vehicle is shown in Figure 5. e battery system provides power. e motor system drives the vehicle. e electronic control system sends commands to the motor controller to control the motor according to the accelerator pedal and brake pedal signal input by the driver. e life of the entire vehicle is related to the life of the three subsystems: battery system, motor system, and electronic control system. It can be determined by statistical analysis methods, simulation methods, and data-driven methods.
Statistical analysis methods mainly include series system, parallel system, and series-parallel system reliability analysis. Cheng et al. and Dong et al. [43,44] studied the reliability analysis and life estimation of a two-component series system. Gu [45] studied the parameter estimation problem of series systems subject to discrete binary geometric distribution. Dong [46] studied the reliability equivalent factor of series systems under the gamma distribution. Zhang and Chang and Sadegh [47,48] discussed the estimation of the remaining useful lifetime of parallel systems with independent and different distributed structures. Li et al. [49] studied the random comparison of the life of series-parallel systems for a series-parallel system composed of N components with random numbers and N components with nonrandom numbers.
In terms of simulation methods, the Monte Carlo method is widely used for reliability analysis due to its robustness and ability to solve complex failure area problems [50]. However, it is inefficient to solve the problem of large number of random variables and small probability. In addition, the Monte Carlo method cannot calculate the multiple failure function combination equation.
Data-driven methods mainly include support vector machines, neural networks, and fuzzy logic. Data-driven methods require a large amount of data to support them. Data acquisition and data quality are particularly important in this method.

Real-Time Life Prediction of Electric
Vehicles in Use e remaining useful lifetime of electric vehicles can be predicted using the real-time electric vehicle data, which can be used to monitor and evaluate the status of the electric vehicle. e remaining useful lifetime is defined as the time interval from the current moment to the predicted failure according to the current operating conditions [51,52], also known as the remaining life. A typical life curve is shown in Figure 6, which can be divided into three stages. e first stage is the early failure period, in which the fault shows a tendency of degradation. is stage can be found through detection data. e second stage is the effective life period.
ere is no early failure of the system. In this stage, the vehicle will stay in a normal working condition. e third stage is the loss failure period, in which the system is under the process of degradation to failure. ere is an abnormal time point at the end of the second stage, which is generally called a symptom point.

Real-Time Operating Data Collection of Electric Vehicles.
Real-time operating data is divided into three categories: battery, motor, and electronic control system.
(1) Data acquisition of the battery system.
Battery system is the power source of the vehicle, and it provides the energy to drive the vehicle. e battery system collects the basic attributes and realtime status of the battery including the number of  battery packs, the number of single batteries, the voltage value of the single battery, the battery pack temperature, the state of charge and discharge, the total voltage, the total current, the State of Charge (SOC), the insulation resistance, and the battery failure. e battery pack is also called a battery box, and each battery box contains a fixed number of single batteries. (2) Data acquisition of the motor system e motor system is mainly composed of a highvoltage motor that provides torque for the vehicle. In other words, it gives the force to drive the vehicle.
e motor system mainly collects information such as the number, temperature, and speed of the driving motors.
(3) Data acquisition of the electronic control system Although batteries and motors are indispensable, the electronic control system is much more complex. It plays a central role in energy vehicles, and its main function is to collect all kinds of signals from accelerator, brake pedal, steering wheel, and so on and send out corresponding instructions according to the information. e data collected from the electronic system including the output from steering control, power drive control, braking control, CAN management control, and vehicle status monitoring display. For example, vehicle speed gear position, accelerator pedal position, brake pedal position, and air-conditioner temperature. e electronic control system is a core part of the electric vehicles.

Prediction of the Remaining Life of Electric Vehicles under
Real-Time Operation. By using various data collected from systems, various algorithms, and intelligent models (such as physical models, neural networks, data fusion, fuzzy logic, and expert systems), the state of the system of electric vehicle is monitored, predicted, and managed. e system is always under monitoring so that the faults can be predicted before this system fails. Meanwhile, a series of maintenance advice is condition-based maintenance. e flowchart is shown in Figure 7. ere are many parameters of the real-time data collected by electric vehicles, so feature extraction is necessary. Feature extraction is to select important indicators as input to the prediction model. Common feature extraction methods can be classified into three categories: filtering, wrapper, and embedding [53]. e filtering method performs feature extraction on the data first, and then filtered features are used to train the model. e main idea of feature extraction is to assign weights to the features in each dimension and then rank them according to the weight. e main methods are the Pearson correlation coefficient method and mutual information coefficient method. Wrapper method directly integrates feature extraction and  Advances in Civil Engineering model training and uses the performance of the model as an evaluation criterion for the feature subset, such as GA algorithm. e embedding algorithm includes a feature extraction process, in which the feature extraction is performed by itself during the training of the learner, such as a decision tree and a random forest.
After the extraction of important indicators, the remaining useful lifetime of electric vehicles can be predicted through inference algorithms and intelligent models. Refer to the life prediction method in the previous section "Life Prediction Methods," and the remaining useful lifetime prediction methods of the system are divided into physical model methods and data-driven methods [54]. e support vector regression-particle filter is used to predict the remaining useful life of battery [55], and many other machine learning methods are used as well such as neural networks. For each subsystem and component, the prediction models and methods overlapped in many ways except the difference in input data. As for the vehicle remaining useful life prediction, like we introduced before, it needs a model to integrate the information from subsystems to get a final result.

Fault Diagnosis of Electric Vehicle Detection Process
When an electric vehicle malfunction occurs, detection is required to determine the malfunctioning system or component for repair and maintenance. As introduced in Section 3.3, fault diagnosis methods can be divided into model-based fault diagnosis and data-based fault diagnosis. In the early stage of the establishment of the electric vehicle fault database, fault diagnosis based on big data is difficult to implement, and the prediction will have a low accuracy rate. A method based on small sample data is needed to achieve the fault diagnosis at this stage. After the accumulation of data, big data analytical methods can be used to find out the fault pattern.

Fault Diagnosis Based on Quantitative Model.
By establishing a mathematical model and comparing the residuals between the model output and the standard output, we can diagnose the occurrence of faults and the corresponding fault types. Residual generation method can be divided into parameter estimation, state estimation, and synchronization state parameter estimation. Parameter estimation includes methods such as least squares and boundary parameter analysis. Common methods for state estimation include fault detection methods based on Kalman filtering, interval observers, and set theory. Taking the battery system as an example, the common indicators of the single cell are temperature and voltage. Parameter estimation is the simplest way to identify the temperature and voltage anomaly of single cell. Existing standards require electric vehicles to record and upload the information of minimum single voltage, the maximum cell voltage, the minimum cell temperature, and the maximum cell temperature. rough parameter estimation methods, a quantitative model can be established to perform temperature and voltage consistency analysis and fault detection on the battery.

Fault Diagnosis Based on Small Sample
Data. At present, there are two ways to solve the problem of small-scale sample data analysis: (1) expand the sample data by random algorithms, and then analyze them by traditional data analysis methods; (2) use Bootstrap method, Bayes method, Monte Carlo method, and other analysis methods suitable for small-scale data. However, each method has its limitations.
e Bootstrap method neutron sample has a large impact on the estimated parameters, and it is easy to generate parameter faults, causing inaccuracies in the estimation [56]. e accuracy of the Monte Carlo method depends on the mathematical model of the parent. e Bayesian method [57] is more suitable for processing small sample data. When the number of samples is small, the probability of failure can be better estimated, but its prior probability will greatly affect the posterior result. Support vector machine algorithm is also a commonly used machine learning method. It supports fault diagnosis under small sample data and is suitable for fault diagnosis of battery systems.
Next, an instance of fault identification will be given. Taking a motor system as an example, we first combine the  (1) Fit the Weibull distribution through small sample fault data to obtain shape parameters and scale parameters (2) Substitute the estimated parameters into the model, obtain bootstrap subsamples by Monte Carlo sampling, and estimate the parameters of each group of subsamples as the prior information of Bayesian (3) Obtain the posterior probability density distribution function of system faults through the Bayesian model e failure probability density of the Weibull distribution is e reliability function is e failure rate function is where t is time, m is the shape parameter, and η is the scale parameter. A set of shape parameters and scale parameters can be determined by regression analysis on a set of fault data and then sampled by the sampling formula [57]: Assume that each group of samples obtained by sampling is independent of each other; then, we can perform parameter estimation on them to obtain the parameter estimates of each group. In this way, the reliability function and the failure probability density function are obtained through the Bayesian formula.

Fault Diagnosis Based on Big Data.
With the rapid development of big data and electric vehicles, fault detection and diagnosis of electric vehicles based on big data has a wide application prospect. A commonly used method is Artificial Neural Network (ANN). e basic idea is based on a large number of electric vehicle fault data, with the fault data dimension as the network input layer dimension, and the fault category as the output dimension. rough continuous iterative training, the model weights, thresholds, and other parameters are obtained. en, we can fit the potential relationship between the sample data and the sample label, realize the fault detection and diagnosis, and constantly update the diagnosis database improvement.
e power battery and motor system of electric vehicles are both nonlinear systems. Traditional neural networks cannot adapt well to nonlinear data. erefore, neurons need to be delinearized by adding activation functions. Common activation functions include ReLU function, sigmoid function, and tanh function, and they are shown as follows: Common faults of power batteries include abnormal temperature, abnormal voltage, and abnormal temperature and voltage consistency. Common faults in motor systems include abnormal output power. e model needs to determine which type of failure has the greatest probability of occurrence among multiple failure types. Now, the softmax function needs to be introduced: soft max y i � e y i n j�1 e y i .
e softmax function converts the output of the neural network into a probability distribution to calculate the cross entropy or directly selects the fault type with the highest probability as the final fault diagnosis result.

Conclusion
In this paper, a complete electric vehicle fault prediction and health management system is established based on the system structure of electric vehicles and the sources of data information. e system mainly includes three levels: (1) Life prediction of electric vehicles at the factory stage: at the factory stage, a physical model method or a data-driven method is used to obtain the life curve of a single component. Based on this, a linear and nonlinear fusion method is used to obtain the life curve of a system composed of multiple components. e simulation method and the data-driven method are used to obtain the vehicle life prediction curve composed of multiple systems. (2) Real-time life prediction of running electric vehicles: the data information to be collected from the three electric vehicle systems under real-time running status is summarized. And the filter information, filter type, wrapper type, and embedded type are used to realize the feature extraction of the data information to predict the remaining life of the electric vehicle. Advances in Civil Engineering e electric vehicle PHM system proposed in this article can realize the whole-life-cycle prognostics and health management of electric vehicles since it supports the detection and prediction for vehicles at factory stage and postfactory stage. is will improve the safety level of electric vehicles and provide reference for the design of other product health management systems in the future. Meanwhile the vehicle users, manufactures, and government can also get benefit from it. For users, the PHM system can help them monitor the status of electric vehicles and identify possible faults, so as to improve the safety level of vehicles. For manufacturers, the PHM system is enabled to establish a more complete vehicle management system, especially in the postfactory stage. For the government, the supporting database of the system can promote research innovation and big-data-related services, which is beneficial to the development of the industry.

Data Availability
No data were directly used in this study. However, in the design of this paper, we refer to the dataset which comes from the real-time status data record of Shenzhen electric buses. e dataset has not been made available because this dataset in not open yet.

Conflicts of Interest
e authors declare that there are no conflicts of interest regarding the publication of this paper.