Integrated Path Planning and Speed Control for Electric Vehicles Using MOPSO-Based Optimization

With the increasing penetration level of electric vehicles (EVs), intelligent control strategies have drawn more and more attentions to make the life of batteries last longer while keeping driving performance. Conventional path planning and speed control operations in EVs are usually independently considered, resulting in performance with respect to energy consumption, riding time and vehicle maneuver. In this work, to tackle the trade-off relations between energy consumption, travel time, and ride comfort, we present an integrated approach by introducing the MOPSO as an integrated optimization engine to solve both path planning and velocity planning simultaneously. The approach also takes into account multiple competing objectives (such as minimal energy consumption, total travel time and vehicular stability) of both optimizing the vehicle path and its corresponding velocity profile. Optimization Complexity: The optimization adopted is a particle swarm– based evolutionary algorithm that is modified to handle several objectives, enabling a Pareto-optimal solution set to be generated that yields flexible trade-offs based on operations preference. The system takes into consideration not only the road gradient, traffic condition, speed limit and battery SOC, but also dynamic constraints for acceleration, deceleration, and regenerative braking. Simulations are performed on a representative urban road topology developed in MATLAB/Simulink by considering an average electric vehicle dynamics and traffic conditions. An integrated MOPSO- based control strategy is compared with shortest-path routing and rule-based speed control approach. Results demonstrate that the proposed methodology enables energy consumption reductions of 17% in average, efficiency gains of around 10% in travel times and more smoothly profiled accelerations contributing for increased levels of comfort. Moreover, the MOPSO methodology shows flexibility with respect to different driving conditions and EV settings. As well as the energy and performance advantages, the system is capable of decision-making under alternative operational objectives, allowing for real time controlled optimization according to driving mode preferences, such as eco-driving or fast commuting mode. It is also compatible with the current vehicle communication and navigation systems enabling it for easy deployment in reallife intelligent Transportation networks with EV platforms. This paper demonstrates the significance of integrated control strategies in improving the performance of the EVs, and shows the prospects of bio-inspired evolutionary multi-objective optimization methods (such as MOPSO) in promoting sustainable urban mobility. The approach was demonstrated to be scalable and flexible to be suitable for next generation control systems for EVs which is in line with the objective of smart city-based and energy-aware transportation planning.