Energy optimal control of powertrain and air conditioning systems for connected EVs

This paper is concerned with the optimal control strategies for the longitudinal control, powertrain, and air conditioning (A/C) system of connected four-wheel hub-drive electric vehicles (EVs). A hierarchical control framework is developed to enhance the energy economy of the vehicle. Real-time connected information is utilized in the upper layer to determine the travel mode. Then, a multi-objective motion planning system (MOMPS) is proposed to plan the optimal acceleration trajectory. In the lower layer, an offline global optimization approach is employed to find the torque combinations that minimize the total power loss. The proposed A/C controller operates based on the bi-level model predictive control (Bi-level MPC) algorithm. A novel prediction model is developed to enable the A/C system to decrease energy consumption by leveraging the speed of the vehicle. The performance of the MOMPS is evaluated using urban test road data, demonstrating that the MOMPS can balance multiple objectives compared to global dynamic programing (Global DP) and the intelligent driver model (IDM). In addition, the proposed torque distribution strategy results in a 4.98% energy-savings rate through comparison with the even torque distribution strategy. Moreover, the A/C controller proposed in this paper can optimize energy consumption by 13.57% compared to a baseline strategy that maintains a constant setting.

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A Terminal-Velocity Heuristic Method for Speed Optimization of EVs in Multi-Intersection Scenarios

Hao,

Zhang,

Jiang

et al. 2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">The optimization of speed holds critical significance for pure electric vehicles. In multi-intersection scenarios, the determination of terminal velocity plays a crucial role in addressing the complexities of the speed optimization problem. However, prevailing methodologies documented in the literature predominantly adhere to a fixed speed constraint derived from traffic light regulations, serving as the primary basis for the terminal velocity constraint. Nevertheless, this strategy can result in unnecessary acceleration and deceleration maneuvers, consequently leading to an undesirable escalation in energy consumption. To mitigate these issues and attain an optimal terminal velocity, this paper proposes an innovative speed optimization method that incorporates a terminal-velocity heuristic. Firstly, a traffic light state model is established to determine the speed range required to avoid coming to a stop at signalized intersections. Subsequently, by addressing the effect of vehicle spacing and utilizing a fitted model of energy consumption within the permissible speed range, an optimal cruising speed trajectory that minimizes energy usage is derived. Finally, the optimal cruising speed becomes a terminal velocity constraint term in the optimization problem, which is solved using a model predictive control algorithm. To assess the effectiveness of the proposed speed planning algorithm, a benchmark algorithm is selected for comparison, which traverses a traffic intersection at the onset of the green light window. Both the proposed algorithm and the benchmark algorithm are tested in two distinct traffic flow scenarios, and the results demonstrate that the proposed optimization algorithm achieves energy savings of 1.5704% and 1.1583%, respectively, making it energy efficient.</div></div>

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Energy optimal control of powertrain and air conditioning systems for connected EVs

Cited by 3 publications

References 9 publications

Human-like Eco-driving Strategy of Electric Vehicle Considering Vehicle-to-everything Communication

Human-like Eco-driving Strategy of Electric Vehicle Considering Vehicle-to-everything Communication

Cooperative energy optimal control involving optimization of longitudinal motion, powertrain, and air conditioning systems

A Terminal-Velocity Heuristic Method for Speed Optimization of EVs in Multi-Intersection Scenarios

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