IIT Mandi, RBEI researchers develop algorithms to predict functioning of IC engines

The developed algorithm can be programmed and be a part of the Electronic Control Unit (ECU) installed in the vehicles.

Autocar Pro News Desk By Autocar Pro News Desk calendar 05 Apr 2021 Views icon27644 Views Share - Share to Facebook Share to Twitter Share to LinkedIn Share to Whatsapp

In an effort to further optimise internal combustion engined (ICE) vehicles, researchers from the Indian Institute of Technology Mandi (IIT Mandi), along with Robert Bosch Engineering and Business Solutions (RBEI), Bangalore, have developed algorithms to predict the functioning of vehicular IC engines so that their operation can be optimised for maximum fuel efficiency and minimum emissions.

A research team lead by Dr. Tushar Jain, assistant professor, School of Computing and Electrical Engineering, IIT Mandi, has published this research in the International Journal of Systems Science, Taylor & Francis. The paper is co-authored by his research scholar, Ms. Vyoma Singh, along with Dr. Birupaksha Pal from Robert Bosch Engineering and Business Solutions, Bangalore.

According to the report, the IC engine that is fuelled by petrol and diesel powers about 99.8% of global transport and, in doing so, generates about 10% of the world’s greenhouse gas (GHG) emissions. While alternatives including battery electric vehicles (BEVs) and other fuels like biofuels and hydrogen are slowly gaining ground, as of now, they are often used in conjunction with conventional IC engines. It is therefore imperative that IC engines designs are optimised in order to ensure the best fuel economy and minimal emissions over the entire lifespan of the engine.

Dr. Jain said, “At any point of time, the working condition of the engine and other devices/systems inside the vehicle should be precisely known, for which, we need the information on several important engine parameters.”

If the information of all the relevant parameters were known, then by continuous monitoring and computation of these parameters, the driver could use the usual driving manoeuvres such as changing the gear appropriately to improve the vehicle's performance. From the technical viewpoint, designing the optimum performing engine depends on the precise knowledge of the system states and the engine parameters; for example, in petrol engines, an air-fuel ratio (AFR) value of 14.67 translates to complete combustion of the fuel, and thus minimal emissions and maximum power. While new vehicles out of the assembly line meet many of the requirements, as they age, the operational parameters change, and the vehicle’s operation becomes less than optimal.

“Due to the high frequency moving parts and operating conditions of the engine, it is difficult to place or install the sensors that are available in the market to measure all the key parameters continuously. We have developed a new algorithm for their online estimation, which will be used to develop advanced, sophisticated controllers for better engine performance” explains Dr. Jain.

The proposed algorithm is based on the unscented Kalman filter and recursive least-squares mathematical techniques to accurately estimate the engine dynamics and parameters. The researchers have benchmarked the performance of their methodology by comparing it with that of the state-of-the-art estimation methods. The numerical stability and robustness of their proposed methodology are analysed through rigorous Monte Carlo simulations and found to be superior to other methods.

The researchers have estimated the spark-ignition engine dynamics, namely the intake manifold pressure, engine speed, and the airflow rate past the throttle, along with the estimation of the engine parameters that determine the said dynamics accurately. The developed algorithm can be programmed and be a part of the Electronic Control Unit (ECU) installed in the vehicles.

The algorithm developed by the IIT Mandi team will help in on-board monitoring and control for IC engines. The application of the developed algorithm can be extended to determine other variables such as the State-of-Charge (SoC) in battery-operated vehicles in real-time as well.

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