Optimal Onboard Battery Charging Strategy for Hotel Load Management in Mild-Hybrid Heavy-Duty Truck

<div>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.</div> <div>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.</div>