Parallel Hybrid Electric Vehicle Model Research Articles

Parallel Hybrid Electric Vehicle Modelling and Model Predictive Control

This paper presents the modelling and calculations for a hybrid electric vehicle (HEV) in parallel configuration, including a main electrical driving motor (EM), an internal combustion engine (ICE), and a starter/generator motor. The modelling equations of the HEV include vehicle acceleration and jerk, so that simulations can investigate the vehicle drivability and comfortability with different control parameters. A model predictive control (MPC) scheme with softened constraints for this HEV is developed. The new MPC with softened constraints shows its superiority over the MPC with hard constraints as it provides a faster setpoint tracking and smoother clutch engagement. The conversion of some hard constraints into softened constraints can improve the MPC stability and robustness. The MPC with softened constraints can maintain the system stability, while the MPC with hard constraints becomes unstable if some input constraints lead to the violation of output constraints.

Ensemble Reinforcement Learning-Based Supervisory Control of Hybrid Electric Vehicle for Fuel Economy Improvement

This study proposes an ensemble reinforcement learning (RL) strategy to improve the fuel economy. A parallel hybrid electric vehicle model is first presented, followed by an introduction of ensemble RL strategy. The base RL algorithm is $Q$ -learning, which is used to form multiple agents with different state combinations. Two common energy management strategies, namely, thermostatic strategy and equivalent consumption minimization strategy, are used as two single agents in the proposed ensemble agents. During the learning process, multiple RL agents make an action decision jointly by taking a weighted average. After each driving cycle iteration, $Q$ -learning agents update their state-action values. A single RL agent is used as a reference for the proposed strategy. The results show that the fuel economy of the proposed ensemble strategy is 3.2% higher than that of the best single agent.

Improving the Performance Attributes of Plug-in Hybrid Electric Vehicles in Hot Climates through Key-Off Battery Cooling

Ambient conditions can have a significant impact on the average and maximum temperature of the battery of electric and plug-in hybrid electric vehicles. Given the sensitivity of the ageing mechanisms of typical battery cells to temperature, a significant variability in battery lifetime has been reported with geographical location. In addition, high battery temperature and the associated cooling requirements can cause poor passenger thermal comfort, while extreme battery temperatures can negatively impact the power output of the battery, limiting the available electric traction torque. Avoiding such issues requires enabling battery cooling even when the vehicle is parked and not plugged in (key-off), but the associated extra energy requirements make applying key-off cooling a non-trivial decision. In this paper, a representative plug-in parallel hybrid electric vehicle model is used to simulate a typical 24-h duty cycle to quantify the impact of hot ambient conditions on three performance attributes of the vehicle: the battery lifetime, passenger thermal comfort and fuel economy. Key-off cooling is defined as an optimal control problem in view of the duty cycle of the vehicle. The problem is then solved using the dynamic programming method. Controlling key-off cooling through this method leads to significant improvements in the battery lifetime, while benefiting the fuel economy and thermal comfort attributes. To further improve the battery lifetime, partial charging of the battery is considered. An algorithm is developed that determines the optimum combination of key-off cooling and the level of battery charge. Simulation results confirm the benefits of the proposed method.

Optimisation and comparison of different powertrain layouts for parallel hybrid electric vehicles equipped with continuous transmission

The development of an efficient hybrid electric vehicle (HEV) platform needs an optimal design of its powertrain layout. This study is concerned with parallel HEVs (PHEVs) equipped with continuous transmission. First, different types of continuous transmissions, including continuously variable transmission (CVT), power-split CVT (PS-CVT) and multi-fixed ratio PS-CVT (MF-CVT), are discussed and their simulation models are presented. Also, various combinations of these continuous transmissions with PHEV configurations (namely, pre-transmission and post-transmission configurations) are introduced. Then, for a given PHEV model, several powertrain layouts resulting from combining the considered continuous transmissions and PHEV configurations are optimised over a standard drive cycle. The optimised powertrain layouts are compared from different perspectives, for instance the vehicle fuel consumption, emissions and some dynamic performance measures. In particular, the sensitivity of the PHEV performance characteristics to the driving cycle pattern, when it is equipped with each optimised layout, is investigated.

Development and Use of Parallel Hybrid Electric Vehicle Model with Suitable Modifications and Break-even Analysis

Development and Use of Parallel Hybrid Electric Vehicle Model with Suitable Modifications and Break-even Analysis

Design and optimization of a new control strategy in a parallel hybrid electric vehicle in order to improve fuel economy

In this paper, a new control strategy for the optimization of the fuel consumption of a parallel hybrid electric vehicle (PHEV) is proposed. A continuously variable transmission (CVT) is employed in the PHEV model. CVT gearboxes have a continuum of gear ratios between their upper and lower limits and their proper control can cause the engine to work optimally. The requested power from the driver and the state of charge of the battery are the controller inputs, while the CVT optimal gear ratio is the controller output. This optimal gear ratio helps the engine to work near its optimal operating points, resulting in better fuel economy and reduced emissions. The controller also attempts to enhance the vehicle performance parameters as much as possible. To investigate the effectiveness of the proposed controller, it is implemented in a PHEV simulated model. Initial results are calculated for three different drive cycles. Afterwards, in order to improve outputs, the initial controller parameters are optimized using multi-objective optimization genetic algorithms. Comparison between the initial and the optimized results showed the success of the optimized control strategy in reducing the fuel consumption, while the vehicle performance and emissions remained within their desired limits.

Controlling torque distribution for Parallel Hybrid Vehicle based on Hierarchical Structure Fuzzy Logic

The Hierarchical Structure Fuzzy Logic Control (HSFLC) strategies of torque distribute for Parallel Hybrid Electric Vehicle (PHEV) in the mode of operation of the vehicle i. e. , acceleration, cruise, deceleration etc. have been studied. Using secondly developed the hybrid vehicle simulation tool ADVISOR, the dynamic model of PHEV has been set up by MATLAB/SIMULINK. The engine, motor as well as the battery characteristics have been studied. Simulation results show that the proposed hierarchical structured fuzzy logic control strategy is effective over the entire operating range of the vehicle in terms of fuel economy. Based on the analyses of the simulation results and driver’s experiences, a fuzzy controller is designed and developed to control the torque distribution. The controller is evaluated via hardware-in-the-loop simulator (HILS). The results show that controller verify its value.