Refine Your Search

Search Results

Viewing 1 to 6 of 6
Technical Paper

The Measures of Improving Power Generation Stability for Harvesting Automobile Exhaust Energy

The automobile exhaust energy can be recovered by the thermoelectric module generator(TEG). Owing to the complex urban traffic, the exhaust gas’s temperature fluctuations are resulted, which means the unstable hot-end temperature of the TEG. By installing solid heat capacity material(SHCM) to the area between the outer wall of the exhaust pipe and the TEG, it is possible to appropriately reduce the temperature fluctuation, but there is still a fluctuation of the TEG’s power output. Then by adding voltage filter circuit (VFC) after the TEG, the power output stability can be improved. This research uses SHCM and VFC to improve the stability of the exhaust gas generation. Firstly, the three-dimensional heat transfer model of the exhaust pipe thermoelectric power generation system is established. The heat capacity materials with low thermal resistance and high heat capacity were selected as the research object based on previous research.
Journal Article

Prediction of Lithium-ion Battery's Remaining Useful Life Based on Relevance Vector Machine

In the field of Electric Vehicle (EV), what the driver is most concerned with is that whether the value of the battery's capacity is less than the failure threshold because of the degradation. And the failure threshold means instability of the battery, which is of great danger for drives and passengers. So the capacity is an important indicator to monitor the state of health (SOH) of the battery. In laboratory environment, standard performance tests can be carried out to collect a number of related data, which are available for regression prediction in practical application, such as the on-board battery pack. Firstly, we make use of the NASA battery data set to form the observed data sequence for regression prediction. And a practical method is proposed to determine the minimum embedding dimension and get the recurrence formula, with which a capacity model is built.
Journal Article

Design of the Linear Quadratic Control Strategy and the Closed-Loop System for the Active Four-Wheel-Steering Vehicle

In the field of active safety, the active four-wheel-steering (4WS) system seems to be an attractive alternative and an effective tool to improve the vehicles' handling stability in lane-keeping control performance. Under normal using condition, the vehicle's lateral acceleration is comparatively small, and the mathematic relationship between the small side force excitation and the small slip angle of the tire is in the linear region. Furthermore, the effects of roll, heave, and pitch motions are neglected as well as the dynamic characteristics of the tires and suspension system in this work. Therefore, the linear quadratic control (LQC) theory is used to ensure that the output of the 4WS control system can keep track of the desired yaw rate and zero-sideslip-angle response can also be realized at the same time.
Technical Paper

Big-Data Based Online State of Charge Estimation and Energy Consumption Prediction for Electric Vehicles

Whether the available energy of the on-board battery pack is enough for the driver’s next trip is a major contributor in slowing the growth rate of Electric Vehicles (EVs). What’s more, the actual capacity of the battery pack depend on so many factors that a real-time estimation of the state of charge of the battery pack is often difficult. We proposed a big-data based algorithm to build a battery pack dynamic model for the online state of charge estimation and a stochastic model for the energy consumption prediction. And the good performance of sensors, high-bandwidth communication systems and cloud servers make it convenient to measure and collect the related data, which are grouped into three categories: standard, historical and real-time data. First a resistance-capacitance ( RC )-equivalent circuit is taken consideration to simplify the battery dynamics.
Journal Article

A Novel Indirect Health Indicator Extraction Based on Charging Data for Lithium-Ion Batteries Remaining Useful Life Prognostics

In order to solve the environmental pollution and energy crisis, Electric Vehicles (EVs) have been developed rapidly. Lithium-ion (Li-ion) battery is the key power supply equipment for EVs, and the scientific and accurate prediction of its Remaining Useful Life (RUL) has become a hot topic in the field of new energy research. The internal resistance and capacity are often used to characterize the Li-ion battery State of Health (SOH) from which RUL is obtained. However, in practical applications, it is difficult to obtain internal resistance and capacity information by using the non-intrusive measurement method. Therefore, it is necessary to extract the measurable parameters to characterize the degradation of Li-ion battery. At present, the methods of extracting health indicators based on measurable parameters have gained preliminary results, but most of them are derived from the Li-ion battery discharging data.
Technical Paper

A Method of Battery State of Health Prediction based on AR-Particle Filter

Lithium-ion battery plays a key role in electric vehicles, which is critical to the system availability. One of the most important aspects in battery managements systems(BMS) in electric vehicles is the stage of health(SOH) estimation. The state of health (SOH) estimation is very critical to battery management system to ensure the safety and reliability of EV battery operation. The classical approach of current integration(coulomb counting) can't get the accurate values because of accumulative error. In order to provide timely maintenance and replacements of electric vehicles, several estimation approaches have been proposed to develop a reliable and accurate battery state of health estimation. A common drawback of previous algorithm is that the computation quantity is huge and not quite accurate, that is updated partially in this study.