Adaptive Model Predictive Control Including Battery Thermal Limitations for Fuel Consumption Reduction in P2 Hybrid Electric Vehicles

Abstract

The primary objective of a hybrid electric vehicle (HEV) is to optimize the energy consumption of the automotive powertrain. This optimization has to be applied while respecting the operating conditions of the battery. Otherwise, there is a risk of compromising the battery life and thermal runaway that may result from excessive power transfer across the battery. Such considerations are critical if factoring in the low battery capacity and the passive battery cooling technology that is commonly associated with HEVs. The literature has proposed many solutions to HEV energy optimization. However, only a few of the solutions have addressed this optimization in the presence of thermal constraints. In this paper, a strategy for energy optimization in the presence of thermal constraints is developed for P2 HEVs based on battery sizing and the application of model predictive control (MPC) strategy. To analyse this approach, an electro-thermal battery pack model is integrated with an off-axis P2 HEV powertrain. The battery pack is properly sized to prevent thermal runaway while improving the energy consumption. The power splitting, thermal enhancement and energy optimization of the complex and nonlinear system are handled in this work with an adaptive MPC operated within a moving finite prediction horizon. The simulation results of the HEV SUV demonstrate that, by applying thermal constraints, energy consumption for a 0.9 kWh battery capacity can be reduced by 11.3% relative to the conventional vehicle. This corresponds to about a 1.5% energy increase when there is no thermal constraint. However, by increasing the battery capacity to 1.5 kWh (14s10p), it is possible to reduce the energy consumption by 15.7%. Additional benefits associated with the predictive capability of MPC are reported in terms of energy minimization and thermal improvement.

A Precise Temperature Control Method for Lithium-ion Battery Pack based on the Nonlinear Model Predictive Control Algorithm

“…Once the output y is obtained, it needs to be fed back to the battery temperature prediction model to correct the temperature field it computes for the existing moment, which is then used as the initial temperature field for the prediction computation for the next moment. The NMPC process can be briefly summarized in three steps: predicting the future temperature of the system, solving the nonlinear planning problem, and the first element of the solution acting on the system [10]. It is specified as follows:…”

Energy Management of P2 Hybrid Electric Vehicle Based on Event-Triggered Nonlinear Model Predictive Control and Deep Q Network

“…The motor torque can be represented by the control signal, while the motor speed in the next sample is obtained via the vehicle speed using Equation (19). Motor efficiency is calculated with a lookup table that includes the motor torque and motor speed.…”

“…An adaptive MPC strategy is proposed in [19], and it includes battery thermal limitations to optimize the fuel consumption of a P2 HEV. The proposed strategy considers the battery's thermal behavior and adjusts the power distribution between the engine and electric motor to reduce fuel consumption while maintaining the battery's thermal limits.…”

Online Temperature-Aware Equivalent Consumption Minimization Strategy for Mild Hybrid Electric Powertrains

“…For full hybrids, active cooling systems are therefore employed to ensure that battery temperature remains within its optimal range, which is typically between 15 and 40 °C [5]. However, for mild hybrids, the relatively high cost of an active cooling system might reduce the cost-effectiveness of hybridization; for this reason, passive cooling systems are often employed [2], [6], [7]. However, the duty cycle of a MHEV battery under an ordinary energy management strategy (EMS) may be highly cyclic and lead to overheating if passive cooling is used [8].…”