Athani, G., Dongare, K., Gavarraju, S., Kulkarni, S. et al., "A Method for Estimating the Improvement in Fuel Economy, for a Vehicle with Intelligent Alternator Control, and Application in Connected Car Systems," SAE Technical Paper 2016-01-0010, 2016, https://doi.org/10.4271/2016-01-0010.
Micro hybrid Systems are emerging as a promising solution to reduce the fuel consumption and greenhouse gas emissions in emerging markets, where the strict emission requirements are being enforced gradually. Micro hybrid Systems reduce the fuel consumption and greenhouse gas emissions in a conventional vehicle with 12 V electrical system, by optimizing the electrical energy generation, storage, and distribution, with functions like Intelligent Alternator Control, Engine Stop/Start, and Load Management.With the advent of Connected Car Systems, information about the vehicle is seamlessly provided to the customer not just through the Human Machine Interface systems within the vehicle, but to other mobile devices used by the customers. In a vehicle with Micro Hybrid System, as the key feature is fuel efficiency improvement, it becomes essential to provide the information of improvement in fuel efficiency, in addition to the fuel consumption, so that the user appreciates the effectiveness of the system. However, real time mapping of the improvement, with respect to a base vehicle is challenging.In this paper, influence of Intelligent Alternator Control system functions, on the fuel economy returned by the vehicle are discussed, before the concept developed for the estimation of improvement in fuel economy. For the estimation of improvement in fuel economy, a novel concept was developed in which the alternator input torque is estimated under the influence of the IAC system, and the engine torque demand is estimated to arrive at an estimate of the total fuel consumption. A virtual base alternator model is implemented for comparing and estimating the improvement in the fuel economy. The concept was validated under controlled conditions and estimations were found to be accurate up to 63%.