Generating realistic data for developing artificial neural network based SOC estimators for electric vehicles

Tracking the state of a lithium-ion battery in an electric vehicle (EV) is a challenging task. In order to tackle one aspect of this task, we choose a data-driven approach for estimating the State of Charge (SOC), which is one of the most import parameters. In this context, the quality of the provided data is of utmost importance. Usually, standardized driving profiles are used to generate current profiles which are then applied to battery cells during testing. However, these standardized driving profiles exhibit significant deviation from real-world conditions, which can considerably affect the learning and validation performance of data-driven approaches. In this paper, we first propose a test profile generator which generates realistic current profiles for EV battery testing. Second, to demonstrate the effect of the proposed test profiles a multilayer perceptron (MLP) based SOC estimator is presented. Finally, we compare the results to the standardized driving profiles

Driving cycle tracking device big data storing and management

Driving cycle is commonly known as a series of speed-time profile. Research on this discipline aids vehicle manufacturing industries in vehicle manufacturing, environmentalists to study on environment quality and profile in accordance to vehicle emissions besides traffic engineers to further investigate the behavior of drivers and the conditions of roads in a certain area or cluster. This also assists automotive industries to innovate energy efficient vehicles which reduce vehicle emissions and energy wastages which lead to air pollution in which a major threat for human health according to Goal 3 of united nations (UN) sustainable development goals (SDG). To construct an accurate driving cycle, data based on real-world driving behavior is crucial and as the world is advancing in technology, the usage of internet of things (IoT) plays an important role in innovatietcons. IoT is an idea of computing every day physical object and information into computers, devices and software. These devices work by using sensors that transmit data to a computer or software allowing them to perform important tasks as needed. In this research, an idea of data collecting device, driving cycle tracking device (DC-TRAD) is constructed with implementation of IoT in which the collected data will be saved into my structured query language (MySQL) database instantly for data storing

Driving cycle development for Kuala Terengganu city using k-means method

Driving cycle plays a vital role in the production and evaluating the performance of the vehicle. Driving cycle is a representative speed-time profile of driving behavior of specific region or city. Many countries has developed their own driving cycle such as United State of America, United Kingdom, India, China, Ireland, Slovenia, Singapore, and many more. The objectives of this paper are to characterize and develop driving cycle of Kuala Terengganu city at 8.00 a.m. along five different routes using k-means method, to analyze fuel rate and emissions using the driving cycle developed and to compare the fuel rate and emissions with conventional engine vehicles, parallel plug-in hybrid electric vehicle, series plug-in hybrid electric vehicle and single split-mode plug-in hybrid electric vehicle. The methodology involves three major steps which are route selection, data collection using on-road measurement method and driving cycle development using k-means method. Matrix Laboratory software (MATLAB) has been used as the computer program platform in order to produce the best driving cycle and Vehicle System Simulation Tool Development (AUTONOMIE) software has been used to analyze fuel rate and gas emission. Based on the findings, it can be concluded that, Route C and single spilt-mode PHEV powertrain used and emit least amount of fuel and emissions

Development of Urban Driving Cycle with GPS Data Post Processing

This paper presents GIS-based methodology for urban area driving cycle construction. The approach reaches beyond the frames of usual driving cycle development methods and takes into account another perspective of data collection. Rather than planning data collection, the approach is based on available in-vehicle measurement data post processing using Geographic Information Systems to manipulate the excessive database and extract only the representative and geographically limited individual trip data. With such data post processing the data was carefully adjusted to include only the data that describe representative driving in Ljubljana urban area. The selected method for the driving cycle development is based on searching for the best microtrips combination while minimizing the difference between two vectors; one based on generated cycle and the other on the database. Accounting for a large random sample of actual trip data, our approach enables more representative area-specific driving cycle development than the previously used techniques.</span

Estimation of electric vehicle battery capacity requirements based on synthetic cycles

The adoption of the Electric Vehicle requires a switch towards a circular system to reduce their environmental impact. Under this framework, the correct sizing of the batteries and avoiding their underuse are key actions. Based on the analysis of real data, this work proposes a model to synthesize current profiles representative of trips containing urban and highway sections. The model is used to generate cycles for common daily driving distances. Different sized batteries are analysed at their beginning and end of life to evaluate their ability to provide the required range. Based on the results, it is suggested that the ongoing trend of battery capacity increase is not justified. The commonly assumed threshold of 70–80% State of Health has proved to be too conservative in most cases, allowing for an extension of the first life that should be individually defined based on functional aspects.Peer ReviewedPostprint (published version

Design, Optimization and Modelling of High Power Density Direct-Drive Wheel Motor for Light Hybrid Electric Vehicles

Throughout the last few years, permanent magnet synchronous motors have been proven suitable candidates for hybrid electric vehicles (HEVs). Among them, the outer rotor topology with surface mounted magnets and concentrated windings seems to be very promising and has not been extensively investigated in literature. In this study, an overall optimization and modelling procedure is proposed for the design and operational assessment of high-power density direct-drive in-wheel motors, targeted towards a light HEV application. The analytical model of an HEV’s subsystems is then implemented for a more accurate evaluation of overall powertrain performance. Furthermore, a simple but effective cooling system configuration, which is taking into account the specific problem requirements, is also proposed