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.
Highlights
Energy management optimization is a key focus of research in the automotive industry as the need to enhance fuel economy and minimise environmental pollution grows
The adaptive model predictive control (MPC) is set to a minimum prediction horizon of p = 2 and with a 0.9 kWh battery of configuration 14s6p and analysed without temperature limitation
To understand the influence of the prediction horizon, the MPC is set to p = 20, and the model performance is analysed without temperature limitations
Summary
Energy management optimization is a key focus of research in the automotive industry as the need to enhance fuel economy and minimise environmental pollution grows. Hybrid electric vehicles (HEVs) continuously grow in popularity due to their contributions in enhancing fuel economy and reducing environmental pollution through the optimum use of dual power sources and regenerative braking [1,2,3,4]. To optimise and control energy consumption, the HEVs splits the power request of the powertrain between the ICE and the electric machine (EM). This often increases the use of the electric machine at low vehicle speeds while the ICE is utilized when needed at high speeds where the fuel efficiency is optimum [3,4]. The sophisticated powertrain of the HEVs complicates the design of the energy control strategy known as the energy management strategy (EMS) [5]
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