Date of Award
Open Access Dissertation
Doctor of Philosophy in Electrical Engineering (PhD)
Administrative Home Department
Department of Electrical and Computer Engineering
Jeffrey B. Burl
Committee Member 1
Committee Member 2
Committee Member 3
Wayne W. Weaver
Committee Member 4
Darrell L. Robinette
Hybrid Electric Vehicles (HEVs) are used to overcome the short-range and long charging time problems of purely electric vehicles. HEVs have at least two power sources. Therefore, the Energy Management (EM) strategy for dividing the driver requested power between the available power sources plays an important role in achieving good HEV performance.
This work, proposes a novel real-time EM strategy for HEVs which is named ECMS-CESO. ECMS-CESO is based on the Equivalent Consumption Minimization Strategy (ECMS) and is designed to Catch Energy Saving Opportunities (CESO) while operating the vehicle. ECMS-CESO is an instantaneous optimal controller, i. e., it does not require prediction of the future demanded power by the driver. Therefore, ECMS-CESO is tractable for real-time operation.
Under certain conditions ECMS achieves the maximum fuel economy. The main challenge in employing ECMS is the estimation of the optimal equivalence factor L*. Unfortunately, L* is drive-cycle dependent, i. e., it changes from driver to driver and/or route to route. The lack of knowledge about L* has been a motivation for studying a new class of EM strategies known as Adaptive ECMS (A-ECMS). A-ECMS yields a causal controller that calculates L(t) at each moment t as an estimate of L*. Existing A-ECMS algorithms estimate L*, by heuristic approaches. Here, instead of direct estimation of L*, analytic bounds on L* are determined which are independent of the drive-cycle. Knowledge about the range of L*, can be used to adaptively set L(t) as performed by the ECMS-CESO algorithm.
ECMS-CESO also defines soft constraints on the battery state of charge (SOC) and a penalty for exceeding the soft constraints. ECMS-CESO is allowed to exceed a SOC soft constraint when an energy saving opportunity is available. ECMS-CESO is efficient since there is no need for prediction and the intensive calculations for finding the optimal control over the predicted horizon are not required. Simulation results for 3 different HEVs are used to confirm the expected performance of ECMS-CESO.
This work also investigates the performance of the model predictive control with respect to the predicated horizon length.
Rezaei, Amir, "An Optimal Energy Management Strategy for Hybrid Electric Vehicles", Open Access Dissertation, Michigan Technological University, 2017.