Hybrid Electric Truck Research Articles

Design, implementation, and experimental validation of optimal power split control for hybrid electric trucks

Hybrid electric vehicles require an algorithm that controls the power split between the internal combustion engine and electric machine(s), and the opening and closing of the clutch. Optimal control theory is applied to derive a methodology for a real-time optimal-control-based power split algorithm. The presented strategy is adaptive for vehicle mass and road elevation, and is implemented on a standard Electronic Control Unit of a parallel hybrid electric truck. The implemented strategy is experimentally validated on a chassis dynamo meter. The fuel consumption is measured on 12 different trajectories and compared with a heuristic and a non-hybrid strategy. The optimal control strategy has a fuel consumption lower (up to 3%) than the heuristic strategy on all trajectories that are evaluated, except one. Compared to the non-hybrid strategy the fuel consumption reduction ranged from 7% to 16%.

Optimal Sizing and Control Strategy Design for Heavy Hybrid Electric Truck

Due to the complexity of the hybrid powertrain, the control is highly involved to improve the collaborations of the different components. For the specific powertrain, the components′ sizing just gives the possibility to propel the vehicle and the control will realize the function of the propulsion. Definitely the components′ sizing also gives the constraints to the control design, which cause a close coupling between the sizing and control strategy design. This paper presents a parametric study focused on sizing of the powertrain components and optimization of the power split between the engine and electric motor for minimizing the fuel consumption. A framework is put forward to accomplish the optimal sizing and control design for a heavy parallel pre‐AMT hybrid truck under the natural driving schedule. The iterative plant‐controller combined optimization methodology is adopted to optimize the key parameters of the plant and control strategy simultaneously. A scalable powertrain model based on a bilevel optimization framework is built. Dynamic programming is applied to find the optimal control in the inner loop with a prescribed cycle. The parameters are optimized in the outer loop. The results are analysed and the optimal sizing and control strategy are achieved simultaneously.

Thermal Behavior of PM in-Wheel Motor used in Off-Road Motor Driven Truck

PM motor is an important energy conversion system for the off-road hybrid electric truck. The motor should be operated and delivered rated power to meet the driving requirement of the truck. For the permanent magnet is sensitive to the temperature variation, this paper presents the thermal behavior of the PM motor based on lumped parameter method. The heat flow caused by the conduction and convection in the PM motor are discussed, a thermal network considering the thermal resistance of the main components of PM motor is built and investigated. The steady-state and transient thermal analysis of the PM motor are calculated. The simulated results are validated with experiment measurement.

Optimal Energy Management in Hybrid Electric Trucks Using Route Information

To benchmark a hybrid vehicle’s Energy Management Strategy (EMS) usually a given, often certified, velocity trajectory is exploited. In this paper it is reasoned that it is also beneficial to optimize the velocity trajectory. Especially optimizing the vehicle braking trajectories, through maximization of energy recuperation, results in considerable fuel savings on the same traveled distance. Given future route (target velocities as function of traveled distance/location), traffic, and possibly weather information, together with the vehicle’s road load parameters, the future power request trajectory can be estimated. Dynamic Programming (DP) techniques can then be used to predict the optimal power split trajectory for the upcoming route, such that a desired state-of-charge at the end of the route is reached. The DP solution is re-calculated at a certain rate in order to adapt to changing conditions, e.g. , traffic conditions, and used in a lower level real-time EMS to guarantee both battery state-of-charge as well as minimal fuel consumption.

Heavy Duty Hybrid Vehicle Evaluations in Utility Fleet Applications

Southern California Edison Company (SCE) has shown a commitment to deploying vehicles in its fleet that meet or exceed emissions standards, emit lower greenhouse gases, and reduce use of petroleum. This has been best demonstrated by operating the nation's largest electric vehicle utility fleet to conduct daily business, as well as demonstrations of hydrogen, fuel cell, and hybrid vehicles. SCE has also for many years been evaluating heavy-duty hybrid technology for use in its fleet. Heavy duty vehicles use a large amount of fuel, and thus even a small percentage savings is significant in terms of total amount of fuel displaced. SCE's fleet uses millions of gallons of fuel per year, much of that being used to run trucks. In addition, the purchase price premium associated with hybrid vehicles is potentially much lower on a percentage basis than with light-duty vehicles. Recently, SCE took part in a national consortium to specify and order a limited number of prototype Class 6 hybrid-electric utility trucks. In conjunction, SCE developed extensive test and evaluation procedures using sophisticated methods and equipment. SCE received its truck in December of 2006. This paper will detail SCE's evaluation procedures and philosophies and give some details on preliminary experiences.

Hybrid component specification optimisation for a medium-duty hybrid electric truck

This paper presents a modelling and simulation approach for determining the optimal degree-of-hybridisation for the drive train (engine, electric machine size) and the energy storage system (battery, ultra capacitor) for a medium-duty truck. The results show that the degree-of-hybridisation of known medium-duty hybrid electric trucks is close to the optimal degree-of-hybridisation using the methods as described in this paper. Furthermore, it is found that the Li-ion battery is from an energy and power density as well as cost point of view the most preferable energy storage system.

Shortest path stochastic control for hybrid electric vehicles

AbstractWhen 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. Copyright © 2007 John Wiley & Sons, Ltd.

Analytical Target Setting: An Enterprise Context in Optimal Product Design

In this article the process of rigorously setting supersystem targets in an enterprise context is explored as a model-based approach termed “analytical target setting.” Engineering design decisions have more value and lasting impact if they are made in the context of the enterprise that produces the designed product. Setting targets that the designer must meet is often done at a high level within the enterprise, however, with inadequate consideration of the engineering design embodiment and associated cost. For complex artifacts produced by compartmentalized hierarchical enterprises, the challenge of linking the target setting rationale with the product instantiation is particularly demanding. The previously developed analytical target cascading process addresses the problem of translating top level design targets into design targets for all systems in a multilevel hierarchically structured product, so that local targets are consistent with each other and top targets can be met as closely as possible. The effectiveness of linking analytical target setting and target cascading is demonstrated in a hybrid electric automotive truck vehicle example. The manufacturer introduces a new product (hybrid electric truck) in the market under uncertainty in fuel prices during the life cycle of the vehicle. The example demonstrates a clear interaction between the enterprise decision making and the engineering product development.

Driving Pattern Recognition for Control of Hybrid Electric Trucks

The design procedure for an adaptive power management control strategy, based on a driving pattern recognition algorithm is proposed. The design goal of the control strategy is to minimize fuel consumption and engine-out NOx and PM emissions on a set of diversified driving schedules. Six representative driving patterns (RDP) are designed to represent different driving scenarios. For each RDP, the Dynamic Programming (DP) technique is used to find the global optimal control actions. Implementable, sub-optimal control algorithms are then extracted by analyzing the behavior of the DP control actions. A driving pattern recognition (DPR) algorithm is subsequently developed and used to classify the current driving pattern into one of the RDPs; thus, the most appropriate control algorithm is selected adaptively. This 'multi-mode' control scheme was tested on several driving cycles and was found to work satisfactorily.

Modelling and control of a medium-duty hybrid electric truck

The main contributions of this paper are the development of a forward-looking hybrid vehicle simulation tool, and its application to the design of a power management control algorithm. The hybrid electric vehicle simulation tool (HE-VESIM) was developed at the Automotive Research Center of the University of Michigan to study the potential fuel economy and emission benefits of the parallel hybrid propulsion system for a medium truck. The fundamental architecture of the feed-forward simulation tool and the dynamic equations of its sub-system modules are first described. A power management control algorithm is then designed and evaluated, which is based on mimicking the behaviour of a dynamic-programming optimisation scheme. Simulation results over an urban driving cycle demonstrate that the hybrid control algorithm is able to improve vehicle fuel economy significantly, compared with the original vehicle, powered only by a diesel engine.

Power management strategy for a parallel hybrid electric truck

Hybrid vehicle techniques have been widely studied recently because of their potential to significantly improve the fuel economy and drivability of future ground vehicles. Due to the dual-power-source nature of these vehicles, control strategies based on engineering intuition frequently fail to fully explore the potential of these advanced vehicles. In this paper, we present a procedure for the design of a near-optimal power management strategy. The design procedure starts by defining a cost function, such as minimizing a combination of fuel consumption and selected emission species over a driving cycle. Dynamic programming (DP) is then utilized to find the optimal control actions including the gear-shifting sequence and the power split between the engine and motor while subject to a battery SOC-sustaining constraint. Through analysis of the behavior of DP control actions, near-optimal rules are extracted, which, unlike DP control signals, are implementable. The performance of this power management control strategy is studied by using the hybrid vehicle model HE-VESIM developed at the Automotive Research Center of the University of Michigan. A tradeoff study between fuel economy and emissions was performed. It was found that significant emission reduction could be achieved at the expense of a small increase in fuel consumption.