Connected Hybrid Electrical Vehicle: Powertrain Optimization Strategy and Experimen

Power-split Hybrid Electric Vehicle (HEV), which accounts for almost 40% of US hybrid-car total sales in 2013, has the ability to store excess energy during driving and braking, and to split the demanded power between the engine and battery. With the advent of connected vehicles, traffic information can be shared and utilized to further optimize HEV's energy use, by predicting the demanded power and optimizing the power-split. However, traffic conditions, and therefore the demanded power, are constantly changing. As a result, the optimization method not only has to account for optimality and charge-sustaining conditions, but also driving-cycle sensitivity and speed of calculation for real-time implementation. This research therefore proposes fast HEV powertrain optimization to improve fuel economy for connected vehicle applications. Additionally, in order to measure the performance of connected vehicle applications, a hardware-in-the-loop system (HiLS), that combines an existing laboratory powertrain research platform with a microscopic traffic simulator, is developed. A computationally-efficient analytical solution to the HEV powertrain optimization problem utilizing vehicle speed prediction based on Inter-Vehicle Communications and Vehicle-Infrastructure Integration is proposed for real-time implementation. First, Gipps' car following model for traffic prediction is used to predict the interactions between vehicles, combined with the cell-transmission-model for the leading vehicle trajectory prediction. Secondly, a computationally efficient charge-sustaining (CS) HEV powertrain optimization strategy is analytically derived and simulated, based on the Pontryagin's Minimum Principle (PMP) and a CS-condition constraint. A 3D lookup-map, generated offline to interpolate the optimizing parameters based on the predicted speed, is also utilized to speed up the calculations. Simulations are conducted for 6-mile and 15-mile cases with different prediction update timings to test the performance of the proposed strategy against a Rule-Based (RB) control strategy on a Toyota Prius engine. Results for accurate-prediction cases show 9.6% average fuel economy improvements in miles-per-gallon (MPG) over RB for the 6-mile case and 7% improvements for the 15-mile case. Prediction-with-error cases show smaller average MPG's improvements, with 1.6% to 4.3% improvements for the 6-mile case and 2.6% to 3.4% improvements for the 15-mile case. For practical purposes, the HEV engine operating range and transient response have to be considered, which introduces additional optimization constraints. Solving a nonlinear optimization problem with constraints analytically is difficult, while numerically is computational heavy and time consuming. Therefore, the nonlinear HEV optimization problem with constraints is expressed and solved as a Separable Programming (SP) problem. First, given the flexibility of the power-split HEV powertrain, the relationship between the minimum fuel consumption and the power-split levels between the engine and battery is calculated and stored offline for all possible vehicle power demands. Therefore, the relationship between HEV power-split levels and engine operating points with minimum fuel consumption for a given vehicle power demand is obtained. Secondly, the problem is formulated with fuel consumption as the cost and power-split level as the optimizing input and solved using SP. In SP, the nonlinear fuel cost and battery charging rate relationships with the power-split levels are approximated as linear-piecewise functions which introduce dimensionless variables that are linear to the input and outputs of the nonlinear functions. The input range constraint and the engine transient dynamics are also formulated. The optimization problem is then solved as a large-dimension linear problem with linear constraints using efficient Linear Programming solvers. The proposed optimization method is then simulated in a receding horizon fashion with various vehicle speed profiles and a case study was tested on a real John Deere diesel engine. Comparable fuel economy with Dynamic Programming is shown with significantly less calculation time and fuel savings of 4.0% and 10.4% over PMP and RB optimizations are observed. A HiLS testbed to evaluate the performance of connected vehicle applications is proposed. A laboratory powertrain research platform, which consists of a real engine, an engine-loading device (hydrostatic dynamometer) and a virtual powertrain model to represent a vehicle, is connected remotely to a microscopic traffic simulator (VISSIM). Vehicle dynamics and road conditions of a target vehicle in the VISSIM simulation are transmitted to the powertrain research platform through the internet, where the power demand can then be calculated. The engine then operates through an engine optimization procedure to minimize fuel consumption, while the dynamometer tracks the desired engine load based on the target vehicle information. Test results show fast data transfer at every 200 milliseconds and good tracking of the optimized engine operating points and the desired vehicle speed. Actual fuel and emissions measurements, which otherwise could not be calculated precisely by fuel and emission maps in simulations, are achieved by the testbed. In addition, VISSIM simulation can be implemented remotely while connected to the powertrain research platform through the internet, allowing easy access to the laboratory setup.