Control System Development for an Advanced-Technology Medium-Duty Hybrid Electric Truck

Lin, C.C.; Peng, Huei; Grizzle, Jessy W.; Liu, Jason; Busdiecker, Matt

doi:10.4271/2003-01-3369

Cited by 68 publications

(32 citation statements)

References 7 publications

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“…The electric machine in the C-ISG system may be packaged around torque converter (using a ring type motor) such as [12], or between the engine and the transmission where the torque converter is removed, such as in Honda IMA system [13,14]. The C-ISG systems can be changed from mild to strong hybridization by adding starter clutch and increasing the power rating of the electric machine and the corresponding battery capacity [15]. Fig.…”

Section: Mild Hybridmentioning

confidence: 99%

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Traction Motor Sizing for Optimal Fuel Economy in Propulsion Hybridization

Liao

Quail²

2012

TOMEJ

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This paper presents the traction motor sizing for optimal urban fuel economy in two mild and three strong hybridization propulsion on front-wheel-drive vehicles. The traction motor sizes, by means of motor rated torque and speed, are optimized for maximum urban fuel economy. The two mild hybrids are Belt-Integrated-Starter-Generator (B-ISG) and Crankshaft-Integrated-Starter-Generator (C-ISG) systems. The three strong hybrid configurations include a strong C-ISG system where motor is placed between a starting clutch and the transmission, one-mode Electric Variable Transmission (EVT), and two-mode EVT. Using the simulated vehicle performance data as constraints and the motor rated torque and speed as the design variables, the objective function is to maximize the urban fuel economy. The purpose of this study is to provide a design guideline for hybrid propulsion configurations and component sizing of the traction motors.

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Section: Mild Hybridmentioning

confidence: 99%

“…The engine may be disconnected from the transmission for maximum regeneration or it may be left connected for ease of control. Eaton hybrid drive unit [15] is a strong C-ISG system coupled with automated manual transmissions that have been implemented in production city buses and medium-duty trucks.…”

Section: Strong or Full Hybridmentioning

confidence: 99%

Traction Motor Sizing for Optimal Fuel Economy in Propulsion Hybridization

Liao

Quail²

2012

TOMEJ

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“…The insights gained from this solution were then used to design a realizable control law and implement it. This design approach achieved significant improvements in fuel economy and emissions reduction over heuristically derived control laws when tested in an Eaton prototype HEV [16].…”

Section: Prior Artmentioning

confidence: 99%

Shortest path stochastic control for hybrid electric vehicles

Tate

Grizzle

Peng

2007

Intl J Robust & Nonlinear

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SUMMARYWhen a hybrid electric vehicle (HEV) is certified for emissions and fuel economy, its power management system must be charge sustaining over the drive cycle, meaning that the battery state of charge (SOC) must be at least as high at the end of the test as it was at the beginning of the test. During the test cycle, the power management system is free to vary the battery SOC so as to minimize a weighted combination of fuel consumption and exhaust emissions. This paper argues that shortest path stochastic dynamic programming (SP-SDP) offers a more natural formulation of the optimal control problem associated with the design of the power management system because it allows deviations of battery SOC from a desired setpoint to be penalized only at key off. This method is illustrated on a parallel hybrid electric truck model that had previously been analyzed using infinite-horizon stochastic dynamic programming with discounted future cost. Both formulations of the optimization problem yield a time-invariant causal state-feedback controller that can be directly implemented on the vehicle. The advantages of the shortest path formulation include that a single tuning parameter is needed to trade off fuel economy and emissions versus battery SOC deviation, as compared with two parameters in the discounted, infinite-horizon case, and for the same level of complexity as a discounted future-cost controller, the shortest-path controller demonstrates better fuel and emission minimization while also achieving better SOC control when the vehicle is turned off. Linear programming is used to solve both stochastic dynamic programs.

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“…There are relatively few results showing how these algorithms perform in practice [2], [3], [8], [11] and how they compare to the existing industrial state of the art.…”

Section: {Jeffcookgrizzle}@umichedumentioning

confidence: 99%

Performance comparison of hybrid vehicle energy management controllers on real-world drive cycle data

Opila

Wang

McGee

et al. 2009

2009 American Control Conference

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Abstract-Hybrid Vehicle fuel economy and drivability performance are very sensitive to the "Energy Management" controller that regulates power flow among the various energy sources and sinks. Many methods have been proposed for designing such controllers. Most analytical studies evaluate closed-loop performance on government test cycles. Moreover, there are few results that compare stochastic optimal control algorithms to the controllers employed in today's production hybrids. This paper studies controllers designed using Shortest Path Stochastic Dynamic Programming (SPSDP). The controllers are evaluated on Ford Motor Company's highly accurate proprietary vehicle model over large numbers of realworld drive cycles, and compared to a controller developed by Ford for a prototype vehicle. Results show the SPSDPbased controllers yield 2-3% better performance than the Ford controller on real-world driving data, with even more improvement on a government test cycle. In addition, the SPSDP-based controllers can directly quantify tradeoffs between fuel economy and drivability.

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Control System Development for an Advanced-Technology Medium-Duty Hybrid Electric Truck

Cited by 68 publications

References 7 publications

Traction Motor Sizing for Optimal Fuel Economy in Propulsion Hybridization

Traction Motor Sizing for Optimal Fuel Economy in Propulsion Hybridization

Shortest path stochastic control for hybrid electric vehicles

Performance comparison of hybrid vehicle energy management controllers on real-world drive cycle data

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