Optimization of rule-based energy management strategies for hybrid vehicles using dynamic programming

Abstract

Reducing energy consumption is a key focus for hybrid electric vehicle (HEV) development. The popular vehicle dynamic model used in many energy management optimization studies does not capture the vehicle dynamics that the in-vehicle measurement system does. However, feedback from the measurement system is what the vehicle controller actually uses to manage energy consumption. Therefore, the optimization solely using the model does not represent what the vehicle controller sees in the vehicle. This paper reports the utility factor-weighted energy consumption using a rule-based strategy under a real-world representative drive cycle. In addition, the vehicle test data was used to perform the optimization approach. By comparing results from both rule-based and optimization-based strategies, the areas for further improving rule-based strategy are discussed. Furthermore, recent development of OBD raises a concern about the increase of energy consumption. This paper investigates the energy consumption increase with extensive OBD usage.

Management System of Smart Electric Vehicles Using Software Engineering Model

“…The simulation results showed the recorded efficiency of the proposed system in terms of batteries charging/discharging and power consumption management. A dynamic programming method was adopted in presenting an optimized rule-based strategy for reducing the consumed power of electric cars [20]. The optimization method adopted the collected data from the cars to decide the optimal way of saving the power with satisfactory level of driving.…”

An Input Linearized Powertrain Model for the Optimal Control of Hybrid Electric Vehicles

“…Many strategies have been proposed in the literature, and they broadly fall into two categories: rule-based approaches and modelbased optimization strategies. While a rule-based control law tends to be simple to implement, it is hard to design and calibrate [2][3][4][5][6][7]. Optimization based approaches on the other hand are easy to specify, but hard to solve, and the solution is usually performed online with limited computational power available [8][9][10][11][12][13].…”

“…subject to (7). Since there are two optimization criteria (minimize fuel use and minimize battery use), scalarization is typically used to create a single cost function…”

Real Drive Truth Test of the Toyota Yaris Hybrid 2020 and Energy Analysis Comparison with the 2017 Model

“…In recent years, the scientific literature has produced several publications on hybrid traction systems. The issues most addressed are those concerning the analysis and optimization of energy flow management in hybrid systems [3][4][5][6][7][8][9][10], the analysis of the performance of components in the hybrid drive system [11][12][13][14][15][16][17], sizing optimal components of the hybrid drive system [18] and the analysis of consumption and greenhouse gas emissions [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. Furthermore, specific models have been developed for the simulation of hybrid drive systems [34], simulation of greenhouse gas emissions [35] and well to wheel (WTW) analysis [36], and other studies analyse how the reliability and reduction of environmental pollution positively influence users in the choice of electrified vehicle [37].…”