ZhiHang Chen

Model-based fault diagnosis in electric drives using machine learning

Electric motor and power electronics-based inverter are the major components in industrial and automotive electric drives. In this paper, we present a model-based fault diagnostics system developed using a machine learning technology for detecting and locating multiple classes of faults in an electric drive. Power electronics inverter can be considered to be the weakest link in such a system from hardware failure point of view; hence, this work is focused on detecting faults and finding which switches in the inverter cause the faults. A simulation model has been developed based on the theoretical foundations of electric drives to simulate the normal condition, all single-switch and post-short-circuit faults. A machine learning algorithm has been developed to automatically select a set of representative operating points in the (torque, speed) domain, which in turn is sent to the simulated electric drive model to generate signals for the training of a diagnostic neural network, fault diagnostic neural network (FDNN). We validated the capability of the FDNN on data generated by an experimental bench setup. Our research demonstrates that with a robust machine learning approach, a diagnostic system can be trained based on a simulated electric drive model, which can lead to a correct classification of faults over a wide operating domain.

Intelligent Hybrid Vehicle Power Control—Part I: Machine Learning of Optimal Vehicle Power

In this series of two papers, we present our research on intelligent energy management for hybrid electric vehicles (HEVs). These two papers cover the modeling of power flow in HEVs, the mathematical background of optimization in energy management in HEVs, a machine learning framework that combines dynamic programming (DP) with machine learning to learn about roadway-type- and traffic-congestion-level-specific energy optimization, machine learning algorithms, and real-time quasi-optimal control of energy flow in an HEV. This first paper presents our research on machine learning for optimal energy management in HEVs. We will present a machine learning framework ML_EMO_HEV developed for the optimization of energy management in an HEV, machine learning algorithms for predicting driving environments, and the generation of an optimal power split for a given driving environment. Experiments are conducted based on a simulated Ford Escape Hybrid vehicle model provided by Argonne National Laboratorys Powertrain Systems Analysis Toolkit (PSAT). Based on the experimental results on the test data, we can conclude that the neural networks trained under the ML_EMO_HEV framework are effective in predicting roadway type and traffic congestion levels, predicting driving trends, and learning optimal engine speed and optimal battery power from DP.

Intelligent Vehicle Power Control Based on Machine Learning of Optimal Control Parameters and Prediction of Road Type and Traffic Congestion

Previous research has shown that current driving conditions and driving style have a strong influence over a vehicles fuel consumption and emissions. This paper presents a methodology for inferring road type and traffic congestion (RT&TC) levels from available onboard vehicle data and then using this information for improved vehicle power management. A machine-learning algorithm has been developed to learn the critical knowledge about fuel efficiency on 11 facility-specific drive cycles representing different road types and traffic congestion levels, as well as a neural learning algorithm for the training of a neural network to predict the RT&TC level. An online University of Michigan-Dearborn intelligent power controller (UMD_IPC) applies this knowledge to real-time vehicle power control to achieve improved fuel efficiency. UMD_IPC has been fully implemented in a conventional (nonhybrid) vehicle model in the powertrain systems analysis toolkit (PSAT) environment. Simulations conducted on the standard drive cycles provided by the PSAT show that the performance of the UMD_IPC algorithm is very close to the offline controller that is generated using a dynamic programming optimization approach. Furthermore, UMD_IPC gives improved fuel consumption in a conventional vehicle, alternating neither the vehicle structure nor its components.

Battery state of charge estimation based on a combined model of Extended Kalman Filter and neural networks

This paper presents our research in battery State of Charge (SOC) estimation for intelligent battery management. Our research focus is to investigate online dynamic SOC estimation using a combination of Kalman filtering and a neural network. First, we developed a method to model battery hysteresis effects using Extended Kalman Filter (EKF). Secondly, we designed a SOC estimation model, NN-EKF model, that incorporates the estimation made by the EKF into a neural network. The proposed methods have been evaluated using real data acquired from two different batteries, a lithium-ion battery U1-12XP and a NiMH battery with 1.2V and 3.4 Ah. Our experiments show that our EKF method developed to model battery hysteresis based on separated charge and discharge Open Circuit Voltage (OCV) curves gave the top performances in estimating SOC when compared with other advanced methods. Secondly, the NN-EKF model for SOC estimation gave the best SOC estimation with and without temperature data.

Multi-objective parameter optimization of a series hybrid electric vehicle using evolutionary algorithms

Hybrid powertrain control strategy and component sizing significantly affect vehicle performance, fuel economy and emissions in hybrid vehicles. Recent research activities in this field show that component sizing and the control strategy are quite intertwined in such a way that concurrent optimization of component sizing and control strategy is warranted. In this paper, we study the total optimization problem in a series HEV and apply evolutionary algorithms to the optimization problem. We will show through experiments that the proposed optimization method has the capability of providing a set of trade-off optimal solutions among the fuel economy and various emissions.

Battery hysteresis modeling for state of charge estimation based on Extended Kalman Filter

This paper presents our research in battery SOC estimation for intelligent battery management. We developed a SOC estimation algorithm based on Extended Kalman Filter to model battery hysteresis effects. The proposed method has been evaluated using data acquired from two different batteries, a lithium-ion battery U1–12XP and a NiMH battery with 1.2V and 3.4 Ah. Our experiments show that our method, which models battery hysteresis based on separated charge and discharge OCV curves gave the top performances in estimating SOC in both batteries when compared with other advanced methods.

Neural Network Approaches for Text Document Categorization

This paper presents our research in text document categorization using neural networks. In text document categorization typically the feature spaces have high dimensions, training data are large and the categories are many. A single neural network is often not sufficient to provide accurate classification or efficient training. We present a hierarchical neural network system and a categorical neural network system for document classification. We will show with an application in engineering diagnostic document categorization that the two proposed systems are more effective and efficient than a single neural network, and the hierarchical neural network system gives the highest accuracy in document categorization.

Neural learning of driving environment prediction for vehicle power management

Vehicle power management has been an active research area in the past decade, and has intensified recently by the emergence of hybrid electric vehicle technologies. Research has shown that driving style and environment have strong influence over fuel consumption and emissions. In order to incorporate this type of knowledge into vehicle power management, an intelligent system has to be developed to predict the current traffic conditions. This paper presents our research in neural learning for predicting the driving environment such as road types and traffic congestions. We developed a prediction model, an effective set of features to characterize different types of roadways, and a neural network trained for online prediction of roadway types and traffic congestion levels. This prediction model was then used in conjunction with a power management strategy in a conventional (non-hybrid) vehicle. The benefits of having the predicted drive cycle available are demonstrated through simulation.

Intelligent vehicle power management using machine learning and fuzzy logic

We present our research in optimal power management for a generic vehicle power system that has multiple power sources using machine learning and fuzzy logic. A machine learning algorithm, LOPPS, has been developed to learn about optimal power source combinations with respect to minimum power loss for all possible load requests and various system power states. The results generated by the LOPPS are used to build a fuzzy power controller (FPC). FPC is integrated into a simulation program implemented by using a generic simulation software as indicated in reference and is used to dynamically allocate optimal power sources during online drive. The simulation results generated by FPC show that the proposed machine learning algorithm combined with fuzzy logic is a promising technology for vehicle power management.

Incremental Learning for Text Document Classification

This paper presents our research in incremental learning for text document classification. Incremental learning is important in text document classification since many applications have huge amount of training data, and training documents become available through time. We propose an incremental learning framework, ILTC(Incremental Learning of Text Classification) that involves the learning of features of text classes followed by an incremental Perceptron learning process. ILTC has the capabilities of incremental learning of new feature dimensions as well as new document classes. We applied the ILTC to a classification system of diagnostic text documents. The experiment results demonstrate that ILTC was able to incrementally learn new knowledge from newly available training data without either referring to the older training data or forgetting the already learnt knowledge.