Title :
DSP Applications in Electric and Hybrid Electric Vehicles [In the Spotlight]
Author :
Akin, Bilal ; Choi, Seungdeog ; Toliyat, Hamid A.
Author_Institution :
Texas Instrum., Houston, TX, USA
fDate :
5/1/2012 12:00:00 AM
Abstract :
As reported by the Web site CBS MoneyWatch [1], electric vehicles are seeing a steady growth in consumer interest, especially within the youngest age group of potential buyers. As is the case with all vehicles, it is very important and even required to continuously monitor its vital equipment. Therefore, today almost all vehicles are equipped with an onboard diagnosis (OBD) system. This system is used for warnings and monitoring critical failures in the vehicle such as ignition, battery, oil and gasoline level, engine, and brakes, among others. If a problem or malfunction is detected, the OBD system sets a malfunction indicator light (MIL) on the dashboard that is readily visible to the vehicle operator and informs the driver of the existing problem. The OBD is a valuable tool that assists in the service and repair of vehicles by providing a simple, quick, and effective way to pinpoint problems by retrieving vital automobile diagnostics. In the case of vehicles with electric motors, the detection of faults expectedly differs from that in vehicles with gasoline engines. This article will describe DSP techniques using Texas Instruments TMS320F2812 signal processor to achieve this.
Keywords :
computerised monitoring; digital signal processing chips; electric motors; failure analysis; fault diagnosis; hybrid electric vehicles; indicators; monitoring; power engineering computing; DSP; OBD system; TMS320F2812 signal processor; Texas instruments; Web site; dashboard; electric motors; equipment monitoring; failure analysis; hybrid electric vehicles; malfunction indicator light; onboard diagnosis system; vital automobile diagnostic; Consumer products; Digital signal processing; Electric vehicles; Fault detection; Hybrid electric vehicles; Monitoring; Noise measurement; Safety; Supply and demand;
Journal_Title :
Signal Processing Magazine, IEEE
DOI :
10.1109/MSP.2012.2185863