Exploring Cognitive Knowledge for Intelligent Vehicle Power Management in Military Mission Scenarios

Growing environmental concerns coupled with the complex issue of global crude oil supplies drive automobile industry towards the development of fuel-efficient vehicles. Due to the possible multiple-power-source nature and the complex configuration and operation modes, the control strategy of a military vehicle is more complicated than that of a conventional vehicle. Furthermore, military vehicles often have heavier weights and are used to operate multiple functions such as engaging weapons, turning on sensors, silent watch, etc., which results in big load fluctuation. In this paper we present our research in optimizing power flow in a heavy vehicle for a given mission plan. A mission plan consists of a sequence of operations and speed profiles. The vehicle architecture will be modeled based on Stryker power system which consists of a diesel engine, a main battery pack, an auxiliary battery pack, and an APU. The APU can supply power to the auxiliary loads and auxiliary batteries only during silent watch mission. We will use PSAT (Powertrain System Analysis Toolkit) construct the vehicle model along with the power system specified above. PSAT is a high fidelity simulation software developed by Argonne National Laboratory under the direction of and with contributions from Ford, General Motors, and Chrysler. PSAT is a "forward-looking" model that simulates vehicle fuel economy and performance in a realistic manner — taking into account transient behavior and control system characteristics. It can simulate a broad range of predefined vehicle configurations (conventional, electric, fuel cell, hybrid electric, light and heavy trucks). We developed a dynamic programming algorithm to optimize the power flow during a given mission. The cognitive knowledge we explored including roadway type prediction and potential load requests associated with specific mission plan.