A Study on Comparison of Energy Management Strategies for a Series Hybrid Tracked Vehicle

After the advancements in the automotive sector, energy management systems for hybrid powertrains are now also being widely investigated for tracked vehicles. This paper presents an overview of several strategies including Dynamic Programming (DP), Equivalent Consumption Minimization Strategy (ECMS), Thermostat Strategy and Model Predictive Control (MPC). These methods are applied to a fictitious tracked vehicle over a predefined driving cycle to demonstrate their main pros and cons; the results show that the offline DP algorithm provides globally optimal solutions, whereas the online ECMS algorithm achieves close results to those of DP. The rule-based Thermostat strategy offers a simple application method; however, it doesn't conduct any optimal control procedure like the others. MPC is quite successful in tracking a State of Charge (SoC) profile however, this approach requires calculation of a reference signal. In another application of the MPC, charge sustaining around a desired SoC can be successively achieved, once a proper cost function is applied.