Data-Driven Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric and Connected Vehicles
Source abstract
Abstract
Advanced energy management strategies (EMS) are used to control the power flow through a vehicle’s powertrain. However, the cost of high-power computational hardware and lack of a priori knowledge of future road conditions poses difficult challenges for engineers attempting to implement globally optimal frameworks. One solution is to use advanced driver assistance systems (ADAS) and connectivity to obtain a prediction of future road conditions. This paper presents a look-ahead predictive EMS which combines approximate dynamic programming (ADP) methods and an adaptive equivalent consumption minimization strategy (A-ECMS) to obtain a near-optimal solution for a future prediction horizon. ECMS is highly sensitive to the equivalence factor (EF), making it necessary to adapt during a trip to account for disturbances. A novel adaptation method is presented in this work which uses an artificial neural network to learn the nonlinear relationship between a speed and the state of charge (SOC) trajectory prediction obtained from ADP to estimate the corresponding EF. A traffic uncertainty analysis demonstrates an approximately 10% fuel economy (FE) improvement over traditional A-ECMS. Using a data-driven adaptation method for A-ECMS informed by a dynamic programming (DP) based prediction results in an EMS capable of online implementation.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Electric and Hybrid Vehicle Technologies · Electric Vehicles and Infrastructure · Vehicle emissions and performance
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Center for Automotive Research, Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH 43212, USA
References (36)
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