Hybrid Electric Vehicles (HEVs) enable fuel savings by re-using kinetic and potential energy that was recovered and stored in a battery during braking or driving down hill. Besides, the vehicle itself can be seen as a storage device, where kinetic energy can be stored and retrieved by changing the forward velocity. It is beneficial for fuel consumption to optimize the velocity trajectory in two ways; i) to assist the driver in tracking an optimal velocity trajectory (e.g. input to an Adaptive Cruise Controller); ii) to estimate the future power request trajectory which can be used to optimize the hybrid components use. Taking advantage of satellite navigation, together with the vehicles current mass and road load parameters, an optimization problem is formulated, and solved for a driver defined time constraint. Despite tight velocity constraints, this can result in 5% fuel saving compared to a Cruise Controller with constant velocity setpoint. The benefit of velocity trajectory optimization is indicated with experimental results.

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Velocity trajectory optimization in Hybrid Electric trucks

Deceleration rates have considerable influence on the fuel economy of hybrid electric vehicles. Given the vehicle characteristics and actual/measured operating conditions, as well as upcoming route information, optimal velocity trajectories can be constructed that maximize energy recovery. To support the driver in tracking of the energy optimal velocity trajectory, automatic cruise control is an important driver aid. In practice, perfect tracking of the optimal velocity trajectory is often not possible. An Adaptive Cruise Control (ACC) system is employed to react to the actual traffic situation. The combination of optimal velocity trajectory construction and ACC is presented as Predictive Cruise Control (PCC).

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Predictive Cruise Control in Hybrid Electric Vehicles

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.

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Optimal Energy Management in Hybrid Electric Trucks Using Route Information

Hybrid vehicles have, at least, two power converters. Usually a prime mover, which can provide tractive power, consuming fuel with an irreversible proces, and secondary power converter(s), which convert tractive power, reversibly, into a power quantity suitable for a storage device, or visa versa. The fuel optimal control of hybrid vehicles involves the control of vehicle velocity, transmission ratio, power split between the prime mover and secondary power converter(s), and stop-start of the prime mover. The potential of hybrid vehicles has not been fully realized due to a lack of control methods that can cope with the unknown future power requests, can be embedded in industry standard hardware, and can obtain fuel use close to a global minimum. The control objective is to drive the vehicle to the next destination with a minimum of fuel subject to a time constraint. The combined control of vehicle velocity, transmission ratio and power split is approximated with a piecewise continuous scalar control signal the combined power request- and optimized with non-smooth optimal control theory. The stop-start of the prime mover and capacity boundaries of the storage device are hereby neglected. Using data from an onboard navigation system, providing information for the upcoming route, e.g., road curvature, road grade, and velocity limitations, the optimal power request, vehicle velocity, transmission ratio and power split trajectories for the upcoming route are obtained. The optimal velocity and transmission ratio trajectories can be used as set points for the real-time velocity (cruise) control and gearshift strategy, also for non-hybrid vehicles. The optimal power request and velocity trajectory can be applied to more involved optimization methods that obtain the optimal power split trajectory, including stop-start of the prime mover, subject to constraints on the storage device capacity boundaries. In case the power split cost function can be approximated with a convex function and there is a monotonically increasing relation between the storage power and the output power of the secondary power converter, a novel numerical approach is applied which is based on observations obtained with the -in optimal control theory well known Pontryagin Maximum Principle. The resulting optimal power split trajectory can be used as set point for the real-time power split controller which gives a robustness against errors in the predicted trajectories. Optimal trajectories can also be used to benchmark and design real-time implementable power split controls, or to derive optimal technology, topology, and component sizes in the design of hybrid vehicle drive trains. In this thesis the optimal hybridization ratio for a long-haul truck is derived for a 513 km long input trajectory. The design of a real-time implementable strategy takes advantage of the results obtained from the necessary conditions of optimality from the previously mentioned Maximum Principle, and boils down to: i) estimation of a multiplier function, that adjoins the energy stored in the storage device to the fuel cost, using real-time available information, and ii) optimization of a locally approximated Hamiltonian like function, given the limited available onboard computational capacity. The optimal control based real-time power split control estimates the multiplier function using linear feedback on an adaptive set point which is based on the energy currently stored in the storage device and the actual kinetic and potential energy of the vehicle. The strategy is implemented in a hybrid electric truck on standard industry hardware. This control is evaluated with experiments on a chassis dynamometer. The controller is easy to tune and obtains a fuel consumption, without a priori knowledge of future power requests, within 1.5% of the global optimum on routes where the capacity boundaries of the storage device are not reached. In case the storage device boundaries are reached, optimal power split trajectories, obtained with data coming from navigation systems, can enhance the performance to become close to optimal. The calculation of optimal trajectories, based on information from a navigation system, the novel numerical solution for scalar optimal control problems with state constraints, and the implemented power split controller adaptive for vehicle mass, vehicle velocity and elevation, together with the observations when predictive information is beneficial, can be seen as the main results of this research.

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Fuel optimal control of hybrid vehicles

The problem of how to stabilize infrequently switching plant is investigated with unfalsified adaptive control approach. A fading memory data cost function is constructed to weaken the effects of timeworn data and its cost-detectablity is proved. A novel controller switching algorithm based on the ideas of self-falsification, stability overlay and resetting is designed. After each plant switching, the algorithm can detect the instability and switch to a stabilizing controller in finite switches and keeps the controller online until next plant switching. For the plant switching infrequently enough, theoretical analysis and simulations illustrate the algorithm can guarantee the cost function of the active controller uniformly bounded.

de A.G. Jager

Papers

Influence of driver, route and vehicle mass on hybrid electric truck fuel economy

Multivariable ellipsoidal unfalsified control