Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Hybrid vehicle techniques have been widely studied recently because of their potential to significantly improve the fuel economy and drivability of future ground vehicles. Due to the dual-power-source nature of these vehicles, control strategies based on engineering intuition frequently fail to fully explore the potential of these advanced vehicles. In this paper, we present a procedure for the design of a near-optimal power management strategy. The design procedure starts by defining a cost function, such as minimizing a combination of fuel consumption and selected emission species over a driving cycle. Dynamic programming (DP) is then utilized to find the optimal control actions including the gear-shifting sequence and the power split between the engine and motor while subject to a battery SOC-sustaining constraint. Through analysis of the behavior of DP control actions, near-optimal rules are extracted, which, unlike DP control signals, are implementable. The performance of this power management control strategy is studied by using the hybrid vehicle model HE-VESIM developed at the Automotive Research Center of the University of Michigan. A tradeoff study between fuel economy and emissions was performed. It was found that significant emission reduction could be achieved at the expense of a small increase in fuel consumption.

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Power management strategy for a parallel hybrid electric truck

Survival Analysis: Techniques for Censored and Truncated Data (2nd ed.) (Book)

Machinery health prognostics: A systematic review from data acquisition to RUL prediction

The issues of global warming and depletion of fossil fuels have paved opportunities to electric vehicle (EV). Moreover, the rapid development of power electronics technologies has even realized high energy-efficient vehicles. EV could be the alternative to decrease the global green house gases emission as the energy consumption in the world transportation is high. However, EV faces huge challenges in battery cost since one-third of the EV cost lies on battery. This paper reviews state-of-the-art of the energy sources, storage devices, power converters, low-level control energy management strategies and high supervisor control algorithms used in EV. The comparison on advantages and disadvantages of vehicle technology is highlighted. In addition, the standards and patterns of drive cycles for EV are also outlined. The advancement of power electronics and power processors has enabled sophisticated controls (low-level and high supervisory algorithms) to be implemented in EV to achieve optimum performance as well as the realization of fast-charging stations. The rapid growth of EV has led to the integration of alternative resources to the utility grid and hence smart grid control plays an important role in managing the demand. The awareness of environmental issue and fuel crisis has brought up the sales of EV worldwide.

A review of energy sources and energy management system in electric vehicles

In this paper, a fuzzy logic controller is developed for hybrid vehicles with parallel configuration. Using the driver command, the state of charge of the energy storage, and the motor/generator speed, a set of rules have been developed, in a fuzzy controller, to effectively determine the split between the two powerplants: electric motor and internal combustion engine. The underlying theme of the fuzzy rules is to optimize the operational efficiency of all components, considered as one system. Simulation results were used to assess the performance of the controller. A forward-looking hybrid vehicle model was used for implementation and simulation of the controller. Potential fuel economy improvement is shown by using fuzzy logic, relative to other controllers, which maximize only the efficiency of the engine.

Fuzzy logic control for parallel hybrid vehicles

A rule-based control and energy management strategy for a series hybrid vehicle is presented. The strategy is based on splitting the power demand between the engine and the battery such that these power sources are operated at high efficiency. The power demand is estimated as the output of a high gain PI controller that controls the longitudinal acceleration of the vehicle. The focus was to improve the fuel economy of the vehicle by suitable power assignment to the power sources. This power split (assignment) is implemented under a rule-base frame. The rules depend on the values of selected variables: the power demand itself, the driver's acceleration command and the status of the SOC (state of charge) of the battery. The rules ensure that the engine and the battery operate at high efficiency whenever possible. At high power demand the engine will operate at its maximum rated power. Simulation results of the proposed strategy showed improvement in fuel economy over the "thermostat" strategy. An improvement of 11% in the urban cycle and of 6% in the highway cycle have been achieved for a series hybrid vehicle driven by a 40 KW diesel engine and a 60 kW lead acid battery.

A rule-based energy management strategy for a series hybrid vehicle

Presents a fuzzy logic based energy management and control strategy for parallel hybrid vehicles. Using the driver commands, the state of charge of the battery, and the motor/generator speed, a set of fuzzy logic control rules has been developed, to effectively split the power between the two powerplants: electric motor and internal combustion engine. The underlying theme of the fuzzy rules is to optimize the operational efficiency of all components, considered as one system. Simulation results are used to assess the performance of the controller. A forward-looking hybrid vehicle simulation model is used to implement the control strategies. Potential fuel economy improvement has been shown by using fuzzy logic, relative to other strategies, which maximize only the efficiency of the internal combustion engine.

Control strategies for parallel hybrid vehicles

Diagnosis classifies the present state of operation of the equipment, and prognosis predicts the next state of operation and its remaining useful life. In this paper, a prognosis method for the gear faults in dc machines is presented. The proposed method uses the time-frequency features extracted from the motor current as machine health indicators and predicts the future state of fault severity using hidden Markov models (HMMs). Parameter training of HMMs generally needs huge historical data, which are often not available in the case of electrical machines. Methods for computing the parameters from limited data are presented. The proposed prognosis method uses matching pursuit decomposition for estimating state-transition probabilities and experimental observations for computing state-dependent observation probability distributions. The proposed method is illustrated by examples using data collected from the experimental setup.

Prognosis of Gear Failures in DC Starter Motors Using Hidden Markov Models

To develop a cost-effective condition-based maintenance strategy, accurate prediction of the Remaining Useful Life (RUL) is the key It is known that many failure mechanisms in engineering can be traced back to some underlying degradation processes This article proposes a two-stage prognostic framework for individual units subject to hard failure, based on joint modeling of degradation signals and time-to-event data The proposed algorithm features a low computational load, online prediction, and dynamic updating Its application to automotive battery RUL prediction is discussed in this article as an example The effectiveness of the proposed method is demonstrated through a simulation study and real data

Mutasim Salman

Papers

Fuzzy logic control for parallel hybrid vehicles

A rule-based energy management strategy for a series hybrid vehicle

Control strategies for parallel hybrid vehicles

Prognosis of Gear Failures in DC Starter Motors Using Hidden Markov Models

Remaining useful life prediction of individual units subject to hard failure