Driving Cycle Prediction Research Articles

Energy management strategy of hybrid electric vehicle using battery state of charge trajectory information

This paper presents a new energy management control strategy of a hybrid electric vehicle, using dynamic programming with preview driving cycle information. Dynamic programming is a promising solution for energy management problem of the hybrid electric vehicle, yet global optimality can only be achieved with preview driving cycle information due to its non-causal property. As recent driving cycle prediction algorithms facilitate the use of preview driving cycle information, rule-based energy management strategy of the hybrid electric vehicle using dynamic programming optimization is presented in this study, under the assumption that the preview driving cycle information is already given. The strategy is composed of dynamic programming calculation and the rule-based control strategy using that calculation. Dynamic programming analyzes optimal control and battery state of charge trajectory in accordance with the vehicle travel distance, with given predicted driving cycle information. The proposed rule-based strategy distributes vehicle’s demand power into the engine and the electric motor, to follow target battery state of charge trajectory acquired from dynamic programming. To validate the control strategy, simulation is conducted on various standard driving cycles. The energy management strategy shows improved fuel economy performance for diverse driving cycles.

Multimode Energy Management for Plug-In Hybrid Electric Buses Based on Driving Cycles Prediction

Driving cycles and road slope are two important factors affecting fuel saving performance of plug-in hybrid electric buses (PHEBs) in Chinese cities. Moreover, onboard auxiliary equipment (e.g., Global Position System receiver and General Packet Radio Service (GPRS) wireless module) of PHEB may provide potential means to communicate with the control center of the bus company, allowing for driving cycle prediction through data communication between foregoing buses and the control center. With this general approach in mind, and by utilizing driving data clustering and driving cycle classifier, this paper presents a multimode switched logic control strategy, targeting fuel economy improvement of the PHEB team for a particular city bus route. First, the normal feature parameters are extracted from the sampled driving history cycles, and the composed feature parameters are given by a mapping of normal feature parameters in this approach. A novel improved hierarchical clustering algorithm is applied for driving cycles' data clustering into four groups. Then, on the basis of the clustering results, support vector machine method is used to predict the current driving cycle. Finally, a switched driving controller is presented according to current type of driving cycle and slope information. Simulation results are compared with those of traditional methods in the given real-world driving cycles of city bus, showing significant improvement, which may offer a theoretical solution with engineering application. Experimental results also demonstrate that the proposed control approach is feasible in the tested bus routes.

An on-line predictive energy management strategy for plug-in hybrid electric vehicles to counter the uncertain prediction of the driving cycle

Predictive energy management could be implemented in real-time with a short period of future driving cycle prediction. However, the completely precise prediction of the future driving cycle remains quite difficult. Two areas of effort have been explored in this study. The first is the implementation of a dynamic-neighborhood particle swarm optimization algorithm in the local optimal energy management strategy of plug-in hybrid electric vehicles based on data from the prediction of the future driving cycle. Second, the influence of an imprecise driving cycle prediction is considered, and then an online correction algorithm is proposed based on the backup control strategy and a fuzzy logic controller. In addition to these efforts, a predictive energy management strategy with an online correction algorithm is finally proposed. Compared with the optimal heuristic method, the presented energy management strategy could reduce the energy by up to 9.7% if the prediction of the future driving cycle is precise. For the situation of imprecise prediction, the online correction algorithm could reduce the deviation from the actual optimal policy by up to 32.39%.

Dynamic Modeling and Driving Cycle Prediction for a Racing Series Hybrid Car

This paper presents Noao, a plug-in series hybrid racing car equipped with an engine/generator set as range extender. To determine the velocity profile, i.e., performance of the car and its power profile, a dynamic model for this car is developed using pedal position as input. This value is easy to measure, representative for race cycles, and presents a novelty. The model is validated with the results from experiments. An analysis based on the map of Magny-Cours racing circuit and drivers pedal action on certain zones of the circuit is formulated and is used as a prediction tool to determine drivers inputs on other racing circuits and generate driving schedules. The results obtained from this analysis are essential to predict energy consumption of the system, estimate driving range, and inspect battery state-of-charge evolution of the car during races. Moreover, the generated driving cycle provides an optimum compromise between drivetime and energy consumption of the system.

An Adaptive Driver Model for Driving Cycle Prediction in the Intelligent Truck

Abstract Intelligent vehicles have significant potential to improve the worldwide traffic situation with regard to both safety and efficiency. Commercial vehicles are an ideal application of intelligent driver assistance systems because of their precisely defined operational limits and professional drivers. For many driver assistance systems, a prediction of the future driving cycle is necessary. This research presents an approach towards predicting the future velocity profile using a gain scheduled driver model together with a longitudinal vehicle model. The parameters of the driver model are estimated during vehicle operation using recursive least squares identification. Assuming repeated operation of the vehicle on the same route, the driver model is supplied with the desired velocity at a particular position and outputs the predicted velocity trajectory. In a case study, the benefit of prediction is shown in a hybrid hydraulic truck with predictive optimized energy management.