Electrification of Diesel-Based Powertrains for Heavy Vehicles

Tyler Swedes, Purdue University

Abstract

In recent decades as environmental concerns and the cost and availability of fossil fuels have become more pressing issues, the need to extract more work from each drop of fuel has increased accordingly. Electrification has been identified as a way to address these issues in vehicles powered by internal combustion engines, as it allows existing engines to be operated more efficiently, reducing overall fuel consumption. Two applications of electrification are discussed in the work presented: a series-electric hybrid powertrain from an on-road class 8 truck, and an electrically supercharged diesel engine for use in the series hybrid power system of a wheel loader. The first application is an experimental powertrain developed by a small start-up company for use in highway trucks. The work presented in this thesis shows test results from routes along (1) Interstate 75 between Florence, KY, and Lexington, KY, and (2) Interstates 74 and 70 east of Indianapolis, during which tests the startup collected power flow data from the vehicle’s motor, generator, and battery, and three-dimensional position data from a GPS system. Based on these data, it was determined that the engine-driven generator provided an average of 15% more propulsive energy than required due to electrical losses in the drivetrain. Some of these losses occured in the power electronics, which are shown to be 82% - 92% efficient depending on power flow direction, but the battery showed significant signs of wear, accounting for the remainder of these electrical losses. Overall, most of the system’s fuel savings came from its regenerative braking capability, which recaptured between 3% and 12% of the total drive energy output. Routes with significant grade changes maximize this energy recapture percentage, but it is shown minimizing drag and rolling resistance with a more modern truck and trailer could further increase this energy capture to between 8% and 18%. In the second application, an electrified air handling system is added to a 4.5L engine, allowing it to replace the 6.8L engine in John Deere’s 644K hybrid wheel loader. Most of the fuel savings arise from downsizing the engine, so in this case an electrically driven supercharger (eBooster) allows the engine to meet the peak torque requirements of the larger, original engine. In this thesis, a control-oriented nonlinear state space model of the modified 4.5L engine is presented and linearized for use in designing a robust, multi-input multi-output (MIMO) controller which commands the engine’s fueling rate, eBooster, eBooster bypass valve, exhaust gas recirculation (EGR) valve, and exhaust throttle. This integrated control strategy will ultimately allow superior tracking of engine speed, EGR fraction, and airfuel ratio (AFR) targets, but these performance gains over independent single-input singleoutput control loops for each component demand linear models that accurately represent the engine’s gas exchange dynamics. To address this, a physics-based model is presented and linearized to simulate pressures, temperatures, and shaft speeds based on sub-models for exhaust temperature, cylinder charge flow, valve flow, compressor flow, turbine flow, compressor power, and turbine power. The nonlinear model matches the truth reference engine model over the 1200 rpm - 2000 rpm and 100 Nm - 500 Nm speed and torque envelope of interest within 10% in steady state and 20% in transient conditions. Two linear models represent the full engine’s dynamics over this speed and torque range, and these models match the truth reference model within 20% in the middle of the operating envelope. However, specifically at (1) low load for any speed and (2) high load at high speed, the linear models diverge from the nonlinear and truth reference models due to nonlinear engine dynamics lost in linearization. Nevertheless, these discrepancies at the edges of the engine’s operating envelope are acceptable for control design, and if greater accuracy is needed, additional linear models can be generated to capture the engine’s dynamics in this region.

Degree

M.Sc.

Advisors

Shaver, Purdue University.

Subject Area

Aerospace engineering|Energy|Transportation

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