On-Board Diagnostics Port Research Articles

Agnostic Battery Management System Capacity Estimation for Electric Vehicles

Battery degradation is a main concern for electric vehicle (EV) users, and a reliable capacity estimation is of major importance. Every EV battery management system (BMS) provides a variety of information, including measured current and voltage, and estimated capacity of the battery. However, these estimations are not transparent and are manufacturer-specific, although measurement accuracy is unknown. This article uses extensive measurements from six diverse EVs to compare and assess capacity estimation with three different methods: (1) reading capacity estimation from the BMS through the central area network (CAN)-bus, (2) using an empirical capacity estimation (ECE) method with external current measurements, and (3) using the same method with measurements coming from the BMS. We show that the use of BMS current measurements provides consistent capacity estimation (a difference of approximately 1%) and can circumvent the need for costly experimental equipment and DC chargers. This data can simplify the ECE method only by using an on-board diagnostics port (OBDII) reader and an AC charger, as the car measures the current directly at the battery terminals.

Cell Fault Identification and Localization Procedure for Lithium-Ion Battery System of Electric Vehicles Based on Real Measurement Data

Vehicle safety risk can be decreased by diagnosing the lithium-ion battery system of electric road vehicles. Real-time cell diagnostics can avoid unexpected occurrences. However, lithium-ion batteries in electric vehicles can significantly differ in design, capacity, and chemical composition. In addition, the battery monitoring systems of the various vehicles are also diverse, so communication across the board is not available or can only be achieved with significant difficulty. Hence, unique type-dependent data queries and filtering are necessary in most cases. In this paper, a Volkswagen e-Golf electric vehicle is investigated; communication with the vehicle was implemented via an onboard diagnostic port (so-called OBD), and the data stream was recorded. The goal of the research is principally to filter out, identify, and localize defective/weak battery cells. Numerous test cycles (constant and dynamic measurements) were carried out to identify cell abnormalities (so-called deviations). A query and data filtering process was designed to detect defective battery cells. The fault detection procedure is based on several cell voltage interruptions at various loading levels. The methodology demonstrated in this article uses a fault diagnosis technique based on voltage abnormalities. In addition, it employs a hybrid algorithm that executes calculations on measurement and recorded data. In the evaluation, a status line comprising three different categories was obtained by parametrizing and prioritizing (weighting) the individual measured values. It allows the cells to be divided into the categories green (adequate region), yellow (to be monitored), and red (possible error). In addition, several querying strategies were developed accordingly to clarify and validate the measurement results. The several strategies were examined individually and analyzed for their strengths and weaknesses. Based on the results, a data collection, processing, and evaluation strategy for an electric vehicle battery system have been developed. The advantage of the developed algorithm is that the method can be adapted to any electric or hybrid vehicle battery.

Empirical Capacity Measurements of Electric Vehicles Subject to Battery Degradation From V2G Services

Accelerated battery degradation, resulting in a reduced capacity, is the main concern when discussing vehicle-to-grid (V2G) services. This paper gives a unique empirical insight into the long-term effects on batteries performing an energy demanding V2G service. To this end, an extensive method for measuring the battery capacity of series produced electric vehicles (EVs) via the DC charge port was developed. The reproducibility of the method is demonstrated with repeated tests on new and aged vehicles and the results are compared with the EVs internal estimates, read via the on-board diagnostics port. The method is also validated with a state-of-the-art battery degradation model. The capacity test method is used to evaluate the battery degradation of a fleet of commercially owned EVs which, as a part of the world's first commercial operation, have been used for V2G services for five years. The EVs have, together with ±10 kW V2G chargers, been delivering primary frequency regulation for 15 hours per day with a daily energy throughput of 50.6 kWh. The usable battery capacity, initially found to be 23 kWh, after two years is reduced on average to 20.7 kWh and after five years is reduced to 18.9 kWh.

Technologies for congestion pricing

Congestion pricing of high-demand roadways seeks to influence travelers’ route choices, trip timing, modes, and destination choices, to keep vehicles moving and avoid excessive congestion. This paper describes the use of various technologies to enable more advanced and cost-effective congestion pricing applications, and a credit-based policy to ensure equitable network access for all travelers.Video-based systems require cameras to capture the state of traffic, plus some form of communication back to users. Both DSRC and cellular-based systems use GPS data to price roads and toll users based on traffic conditions. DSRC employs roadside units (RSUs) to receive and send messages to in-vehicle DSRC units. A cellular-based system could use communications from cellular towers in combination with a smartphone, on-board diagnostics port (OBD-II), or pre-installed cellular chip. DSRC is a valuable technology to pilot congestion pricing at highly congested locations, such as bridges and major highways, while cellular communications enable congestion pricing across entire networks.VMT fees can be relatively simple, or variable in space and time, facilitating transportation-agency cost recovery. A next step for roadway management is congestion pricing (CP), to better reflect the marginal delay costs of one's travel choices. When coupled with travel credits, CP can better ensure welfare gains for most travelers.

Securing the On-Board Diagnostics Port (OBD-II) in Vehicles

Securing the On-Board Diagnostics Port (OBD-II) in Vehicles

Sistema de monitoreo de alcoholemia para control parental

Conducir alcoholizado tiene un impacto directo en la ocurrencia de accidentes de tránsito y constituye un creciente inconveniente que está afectando a la sociedad gravemente. El trabajo busca aplicar un sistema de detección de alcoholemia en conductores, permitiendo la toma de decisiones y acciones sobre la funcionalidad del vehículo. Se establece una plataforma que recolecta datos del puerto OBD (On Board Diagnostics), los procesa en la aplicación de un Smartphone y espera que los mismos cumplan con ciertos requerimientos, para luego solicitarle al conductor que comience el análisis de alcoholemia, presentando los resultados del análisis en tiempo real a través del dispositivo móvil conectado a internet. El aspecto global del sistema lo brinda la plataforma, ya que al estar en la nube, permite acceder desde cualquier dispositivo con conexión a internet, para visualizar, administrar y controlar el vehículo desde cualquier parte del mundo. SMA plantea una arquitectura global y completamente escalable para adquirir datos de cualquier vehículo que cuente con un puerto OBD y un Smartphone con conexión a internet y Bluetooth. Se puede decir que los resultados obtenidos fueron satisfactorios y su funcionamiento fue el esperado.

PREDICTION MODEL FOR POLLUTANTS WITH ONBOARD DIAGNOSTIC SENSORS IN VEHICLES

In this work, a prediction model is developed to illustrate the relationship between the internal parameters of a vehicle and its emissions. Vehicles emit various hazardous pollutants and understanding the influence of in-vehicle parameters is key to reducing their environmental impact. The values of the internal parameters were collected through the On-Board Diagnostics port, while the values of the emissions were measured from the exhaust pipe using Arduino sensors. The observed values were then matched based on the timestamps received from both sources and fit with both linear and polynomial regressions to accurately model the relationship between the internal parameters and pollutants. These models can then be used to estimate vehicle emissions based on the in-vehicle parameters, including vehicle speed, relative throttle position, and engine revolutions per minute. A wide majority of the relationships between various in-vehicle parameters and emissions show no observable correlation. There are observable correlations between carbon dioxide emissions and vehicle speed, as well as carbon dioxide emissions and engine revolutions per minute. These relationships were modelled using linear and polynomial regression with a resulting adjusted R-squared value of approximately 0.1.