Heavy-duty trucks idling during the hotel period consume millions of gallons of
diesel/fuel a year, negatively impacting the economy and environment. To avoid
engine idling during the hotel period, the heating, ventilation, and
air-conditioning (HVAC) and auxiliary loads are supplied by a 48 V onboard
battery pack. The onboard battery pack is charged during the drive phase of a
composite drive cycle, which comprises both drive and hotel phases, using the
transmission-mounted electric machine (EM) and battery system. This is
accomplished by recapturing energy from the wheels and supplementing it with
energy from the engine when wheel energy alone is insufficient to achieve the
desired battery state of charge (SOC). This onboard battery pack is charged
using the transmission-mounted EM and battery system during the drive phase of a
composite drive cycle (i.e., drive phase and hotel phase). This is achieved by
recapturing wheel energy and energy from the engine when the wheel energy is
insufficient to achieve the desired SOC during the drive phase. In the authors’
previous work, a dynamic programming (DP)–based framework is developed that
employs a multi-objective cost function to minimize fuel consumption and
maximize the regeneration to achieve the benchmark results for the SOC
trajectories.
This article discusses the real-time implementable control strategies for the
heavy-duty truck’s hybrid powertrain, including the mode switch and EM torque
for charging. The mode switch is a rule-based control strategy that responds to
the wheel torque demand, while the EM torque’s control can have several
approaches, such as rule-based, optimal charging strategies that are inspired by
equivalent cost minimization strategy (ECMS), or adaptive strategy that updates
the equivalent factor according to the battery SOC state. This work presents and
studies the different choices to control the EM torque and their impact on
vehicle performance and energy consumption. The complete cycle results are
compared with the benchmark results, and the energy analysis is accomplished to
validate the efficacy of the proposed real-time implementable optimal control
strategies (i.e., rule-based and adaptive ECMS). The adaptive optimal control
strategy is the potential candidate to be implemented on a real heavy-duty
vehicle for optimal management of hotel loads, as it produces the SOC trajectory
closer to the benchmark results within the error of ±1.25% while costing minimal
fuel consumption. The fuel saving of 2.96% is achieved when compared to
conventional heavy-duty trucks for each day of a typical highway trip and hotel
phase for each heavy-duty truck, which is 18.2% higher than the rule-based
control strategy.
