IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) e-ISSN: 2278-1684,p-ISSN: 2320-334X, Volume 13, Issue 1 Ver. V(Jan. - Feb. 2016), PP 31-40 www.iosrjournals.org DOI: 10.9790/1684-13153140 www.iosrjournals.org 31 | Page ANN based intelligent energy management control of hybrid vehicle for improving fuel consumption Zoonubiya Ali 1 , Dr.S.L Badjate 2, Dr.R.V.Kshirsagar 3 1 (Electronics & Telecommunication Engineering, Disha Institute of Management and Technology/CSVTU,India) 2 (Electronics & Telecommunication, S.B Jain Institute of Technology management & Research/ N.U, India) 3 (Electronics Engineering, Priyadarshini college of Engineering/ N.U, India) Abstract:Every passing year hybrid electric vehicles are becoming popular. After so much improved result it seems to be one of the potential solutions of global problems like global warming, rise in fuel prices, pollution etc. Due to various non linear parameters which are included in developing hybrid electric vehicles many a time to develop and achieve this new emerging technology, its analytical procedure is time consuming and it requires simplifying assumption. One practical alternative to analytical and empirical method that is easy and more accurate is Artificial Neural Network (ANN). For modeling complex real world problem in many discipline, Artificial Neural Network have emerged as computational modeling tool. To reduce fuel consumption and emission problems environmental condition, driver’s behavior and types of roadway were considered very influential. So to analyze the vehicle’s performance all these factors are incorporate in the system and presented in this paper. Artificial environmental data for elaborating vehicles performance is created artificially by neural network. Model for road way, SOC, vehicle, driver behavior and environment condition were created and ANN is being developed for all models. Key words: Artificial neural network (ANN), fuzzy logic, fuel consumption, hybrid vehicle. I. INTRODUCTION As compared to normal gasoline vehicle, a hybrid vehicle gives less fuel consumption and carbon emissions. Control strategy is the main key objective which is to be held responsible for achieving the improved performance and success of hybrid vehicle. Control strategy oh hybrid vehicle is broadly divided in two types rule based strategy and optimal control strategy for both series and parallel vehicles. Various researchers are using rule based control which is based on human expertise, mathematical and heuristic information. Rules based are again categorized in three different types; rule based which uses human knowledge for writing rules. Fuzzy logic based, which have more robust structure and can provide flexibility to controller because of non linear structure and can easily deal with non linear problem of power splitting between two sources of controller. The last type of rule based control strategy isNeuro-fuzzy which is the combination of fuzzy logic and artificial neuro-fuzzy control. FIGURE 1: OPTIMAL CONTROL STRATEGY Whereas the Optimal control strategy is another category which is further subdivides into global optimization (offline) and real time optimization (online) type. The optimal strategy is perfect and the controller is optimized according to cost function of system. But these controllers are sensitive to noise. Here both static and dynamic behaviors have to be taken into consideration for achieving optimized results. Various researches have been carried out by combining various categories stated above for series as well as for parallel. ANFIS (adaptive neuro-fuzzy inference system) integrate the best features of fuzzy logic and neural network and so it has attracted the interest of researchers to synthesis controllers and to develop the