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The previously developed power-based fuel consumption theory for Internal Combustion Engine Vehicles (ICEV) is extended to Battery Electric Vehicles (BEV). The main difference between the BEV model structure and the ICEV is the bi-directional character of traction motors and batteries. A traction motor model was developed as a bi-linear function of positive and negative traction power. Another difference is that the accessories and cabin heating are powered directly from the battery, and not from the powertrain. The resulting unified model for ICEV and BEV energy consumption has linear terms proportional to positive and negative traction power, accessory power, and overhead, in varying proportions. Compared to the ICEV, the BEV powertrain has a high marginal efficiency and low overhead. As a result, BEV energy consumption data under a wide range of driving conditions are mainly proportional to net traction power, with only a small offset.

Fuel power consumption for light duty vehicles has previously been shown to be proportional to vehicle traction power, with an offset for overhead and accessory losses. This allows the fuel consumption for an individual powertrain to be projected across different vehicles, missions, and drive cycles. This work applies the power-based model to commercial vehicles and demonstrates its usefulness for projecting fuel consumption on both regulatory and customer use cycles. The ability to project fuel consumption to different missions is particularly useful for commercial vehicles, as they are used in a wide range of applications and with customized designs. Specific cases are investigated for Light and Medium Heavy- Duty work trucks. The average power required by a vehicle to drive the regulatory cycles varies by nearly a factor 10 between the Class 4 vehicle on the ARB Transient cycle and the loaded Class 7 vehicle at 65 mph on grade.

Previously fuel consumption on a drive cycle has been shown to be proportional to traction work, with an offset for powertrain losses. This model had different transfer functions for different drive cycles, performance levels, and applied powertrain technologies. Following Soltic it is shown that if fuel usage and traction work are both expressed in terms of cycle average power, a wide range of drive cycles collapse to a single transfer function, where cycle average traction power captures the drive cycle and the vehicle size. If this transfer function is then normalized by weight, i.e. by working in cycle average power/weight (P/W), a linear model is obtained where the offset is mainly a function of rated performance and applied technology. A final normalization by rated power/weight as the primary performance metric further collapses the data to express the cycle average fuel power/rated power ratio as a function of cycle average traction power/rated power ratio.

Medium duty vehicles come in many design variations, which makes testing them all for CO2 impractical. As a result there are multiple ways of reporting CO2 emissions. Actual tests may be performed, data substitution may be used, or CO2 values may be estimated using an analytical correction. The correction accounts for variations in road load force coefficients (f0, f1, f2), weight, and axle ratio. The EPA Analytically Derived CO2 equation (EPA ADC) was defined using a limited set of historical data. The prediction error is shown to be ±130 g/mile and the sensitivities to design variables are found to be incorrect. Since the absolute CO2 is between 500 and 1,000 g/mi, the equation has limited usefulness. Previous work on light duty vehicles has demonstrated a linear relationship between vehicle fuel consumption, powertrain properties and total vehicle work. This relationship improves the accuracy and avoids co-linearity and non-orthogonality of the input variables.

The drive cycle average powertrain efficiency of current US vehicles is studied by applying a first principles model to the EPA Test Car List database. The largest group of vehicles has naturally aspirated engines and six speed planetary automatic transmissions, and defines the base technology level. For this group the best cycle average powertrain efficiency is independent of vehicle size and is achieved by the lowest power-to-weight vehicles. For all segments of the EPA test, the fuel required per unit of vehicle work (the inverse of powertrain efficiency), is found to increase linearly with a basic powertrain matching parameter. The parameter is (D/M)(n/V), where D is engine displacement, M vehicle mass, and (n/V) the top gear engine speed over the vehicle speed. The fuel consumption penalties in the City segments due to powertrain warm-up, aftertreatment warm-up, stop-and-go operation, and power-off operation are estimated.

Electronic control of valve timing and event duration in a camless engine enables the optimization of fuel economy, performance, and emissions at each engine operating condition. This flexible engine technology can offer significant benefits to each of these areas, but optimization techniques become crucial to achieving these benefits and understanding the principles behind them. Optimization techniques for an I4 - 2.0L camless ZETEC dynamometer engine have been developed for a variety of areas including: Cold Starts Cylinder Deactivation Full Load Idle Transient A/F control The procedure for the optimization of each of these areas will be presented in detail, utilizing both steady state and transient dynamometer testing. Experimental data will be discussed and the principles governing the response of the engine will be explained. Selection criteria for determining an optimum strategy for the different modes will be presented and recommendations will be discussed.

In this paper recent control developments for an electromechanical valve actuator will be presented. The model-based control methodology utilizes position feedback, a nonlinear observer that provides virtual sensing of the armature velocity and current, and cycle-to-cycle learning. The controller is based on a nonlinear state-space description of the actuator that is derived based on physical principles and parameter identification. A bench-top experimental setup and a rapid control prototyping system are used to quantify the actuator performance. Experiments are conducted to measure valve release timing, transition times, and contact velocities for open- and closed-loop control schemes. Simulation results are presented for a feed-forward cycle-to-cycle learning controller.