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Range anxiety is one of the major factors to be dealt with for increasing penetration of EVs in current Automotive market. The major reasons for range anxiety for customers are sparse charging infrastructure availability, limited range of Electric vehicles and range uncertainty due to diverse real-world usage conditions. The uncertainty in real world range can be reduced by increasing the correlation between the testing condition during vehicle development and real-world customer usage condition. This paper illustrates a more accurate test methodology development to derive the real-world range in electric vehicles with experimental validation and system level analysis. A test matrix is developed considering several variables influencing vehicle range like different routes, drive modes, Regeneration levels, customer drive behavior, time of drive, locations, ambient conditions etc.

Commercial transportation is the key pillar of any growing economy. Light and Small commercial vehicles are increasing every day to cater the logistics demand, but there is always a gap between customer’s actual and desired operational efficiency. This is because of lack of organized fleet and efficient fleet operation. The major requirement of fleet owners is timely delivery, high productivity, downtime reduction, real time tracking, etc., Automakers are now providing fleet management application in modern LCV & SCV to satisfy the fleet operator requirement. However, any feature malfunction, consignment mismatch, wrong notification, missed alerts, etc., can incur huge loss to fleet operator and disrupt the entire supply chain. Hence it is very critical to extensively validate the telematics features in fleet management application. This paper explains the approach for exhaustive validation strategy of fleet management applications (B2B) from end user perspective.

Fuel economy, performance and drivability are the three important parameters for evaluating the vehicle performance. Powertrain matching plays a major role in meeting the above targets. Fuel economy is measured based on city, highway and some user defined driving cycles which can be considered as real world usage profiles. Performance and Drivability is evaluated based on the in-gear, thru-gear (acceleration performance) and grade-ability performance. The load collective points of the engine greatly influence the engines performance, fuel economy and emissions, which in-turn depends on the N/V ratio of the vehicle. The optimal selection of gear and final drive ratios plays a key role in the optimization of the Powertrain for a particular vehicle. The current study involves dynamic simulation of the vehicle performance and fuel economy at transient engine test-bed for different gear and final drive ratio combinations using AVL DynoExcat-dynamometer.