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Hybrid Electric Vehicle (HEV) Controls Development is an important aspect to realize the goals of Powertrain Electrification i.e. fuel economy and emission improvement. Keeping that in mind, development engineers need to formulate numerous control strategies. Once the control strategy is evaluated and frozen, it typically does not change from one vehicle model application to another. However, it may happen that Electronic Control Unit (ECU) manufacturer may change depending on the sourcing strategy. Therefore, in order to maintain uniformity, it may be required to compare control strategy of a finished ECU product frozen for one model application to be compared with new ECU sourced through another manufacturer. This paper discusses a methodology to compare control strategy of two ECU’s sourced from different ECU manufacturers with identical control requirements.

Battery modeling is of major concern going forward for Hybrid Electric Vehicle (HEV) and Electric vehicle (EV) modeling. The major issue lies in characterizing the battery power, Charge acceptance and reaction to sudden load changes (transient behavior) in relation to battery’s State of Charge (SOC). In particular modeling the battery is challenging task as it requires a lot of test data to understand and validate modeled chemical and electrical characteristics in various operating conditions. Hence, the one of the ways of simulating Battery based Hybrid System is to use battery Hardware-in-the-Loop Simulation (HILS) or simply known as Battery-in-Loop (BIL). With this approach hybrid vehicle or more precisely battery management system (BMS) development time and cost can be significantly reduced by eliminating the detailed battery modeling. To understand the effectiveness of this approach, Battery Hardware-in-Loop test setup was developed.

In this study, an alternate way of development and implementation of control strategy for torque split of a Hybrid Electric Vehicle (HEV) is proposed. The control strategy evaluates each and every operating point for Internal Combustion Engine (ICE) and electric motor-generator (E-machine) corresponding to all possible torque split combinations at present time instant and finally chooses one combination with the least cost function, which is estimated by converting electrical energy into equivalent fuel consumption[2]. Henceforth, the control strategy is able to perform real time iterations to choose the E-machine and ICE torque combinations with least effective fuel consumption also referred herein to as the optimal operating points. As a result, by running the vehicle at optimal operating points, overall fuel consumption over the complete drive cycle is reduced.