Optimal Energy Management for Hybrid Electric Vehicles Based on Dynamic Programming and Receding Horizon

Abstract

Fuel consumption and emissions in parallel hybrid electric vehicles (HEVs) are directly linked to the way the load request to the wheels is managed between the internal combustion engine and the electric motor powered by the battery. A significant reduction in both consumption and emissions can be achieved by optimally controlling the power split on an entire driving mission (full horizon—FH). However, the entire driving path is often not predictable in real applications, hindering the fulfillment of the advantages gained through such an approach. An improvement can be achieved by exploiting more information available onboard, such as those derived from Advanced Driver Assistance Systems (ADAS) and vehicle connectivity (V2X). With this aim, the present work presents the design and verification, in a simulated environment, of an optimized controller for HEVs energy management, based on dynamic programming (DP) and receding horizon (RH) approaches. The control algorithm entails the partial knowledge of the driving mission, and its performance is assessed by evaluating fuel consumption related to a Worldwide harmonized Light vehicles Test Cycle (WLTC) under different control features (i.e., horizon length and update distance). The obtained results show a fuel consumption reduction comparable to that of the FH, with maximum drift from optimal consumption of less than 10%.

Novel Approaches for Energy Management Strategies of Hybrid Electric Vehicles and Comparison with Conventional Solutions

“…The hybrid vehicle considered in this study presents a parallel P3 hybrid powertrain configuration, as already presented in [4]. The base vehicle mass is 1400 kg, and the frontal area is 2 m 2 , whereas the other key parameters required for the simulation are taken from [5].…”

“…The single battery is a LiFePO 4 cell manufactured by A123 systems [9], with a nominal voltage of 3.3 V, a nominal capacity of 1.1 Ah and a weight of 39 g. The transmission is a manual 5-speed manufactured by TREMEC model TR-2450 [10]. The mechanical and electrical transmission components are modelled with a constant behaviour, whereas the efficiencies of the internal combustion engine (SI, 1.2 L, 4 in-line cylinders, 105 Nm at 3500 rpm) and the electrical machine (130 Nm max torque, 9000 rpm max speed, 3200 rpm transition speed) are modelled through maps depending on shaft speed and torque [4]. The main drivetrain transmission parameters are taken from [5,10].…”

“…The main drivetrain transmission parameters are taken from [5,10]. Further details on the hybrid vehicle model used in this work can be found in [4].…”

A Novel Architecture of Multimode Hybrid Powertrains for Fuel Efficiency and Sizing Optimization

“…The optimization problem for DP can be formulated by the choice of control variables at each state to find the optimal value of the objective function. The objective function can be expressed as [28], [31], [32],…”

Optimal Degree of Hybridization for Spark-Ignited Engines with Optional Variable Valve Timings

“…By increasing the load, this operating point shift (OPS) strategy significantly increases the efficiency of the ICE. The optimal operating strategy can be found with noncausal methods as described in [38][39][40][41]. However, due to the high range of feasible input variables, it is difficult to implement a causal operating strategy in an on-road application [42,43], which performs closely to the optimal strategy found by noncausal methods.…”