Taking the complex but regular characteristics of bus routines into account, the stochastic dynamic programming (SDP) might be a method with more potential to optimize the energy management of a plug-in hybrid electric bus (PHEB). However, the discrete transmission system and the continuous power system make it a complicated multidimensional optimal problem, particularly for PHEB with automated mechanical transmission (AMT), and the optimal decisions, which are obtained based on historical data, might not always well satisfy the driver's expectation to vehicle maneuverability under various driving conditions. To solve these problems, an adaptive approach based on the SDP is proposed in this paper. Exhaustively, the SDP is propelled into the input of the transmission to only optimize the torque split under the special gearshift logic, which reduces the dimensions of optimization and obtains more applicable optimal sequences. Then, an adaptive factor, which trades off the vehicle fuel economy and drivability in real time by dynamically adjusting the gearshift points and the torque split, is developed for the variation of the complicated bus driving cycles. The simulation results demonstrate that the proposed method could well respond to the variations of the driving conditions (e.g., road grade and vehicle load). Furthermore, the performance of the proposed method is discussed in detail by comparisons with different control strategies. More importantly, the proposed approach has great potential to be applied in practice.

Application-Oriented Stochastic Energy Management for Plug-in Hybrid Electric Bus With AMT

A design method for the gearshift strategy in powertrain systems is proposed to explore the energy saving potential of an electric vehicle (EV) equipped with a multispeed automated manual transmission (AMT). The optimal gearshift schedule is obtained by solving a nonlinear time-varying optimal problem in the framework of model predictive control, wherein, the vehicle driveability, represented by the driver's power demand satisfaction, and battery efficiency are considered. The solution approach is developed basing on the combination of Pontryagin's minimum principle and numerical methods, in addition to the real-time applications. Simulation results for a passenger EV with four-speed AMT on different drive cycles show that compared with the case of a standard gearshift strategy, an additional fuel saving can reach 3–5% and even more when considering road characteristic such as road slope. Furthermore, hardware in the Loop simulation for experimental validation is also given in this paper. Results indicate that both energy efficiency and computational speed are improved.

On-line Optimal Control of the Gearshift Command for Multispeed Electric Vehicles

The energy management strategy (EMS) of a hybrid electric vehicle is a supervisory control meant to steer power flows among the different energy sources of the powertrain. The EMS is designed for the purpose of reaching the maximum potential of a given powertrain architecture. Fuel consumption is not the only criterion to be considered for evaluating the global performance of an EMS, since the EMS also impacts other features such as drivability, through engine starts/stops and gear shifts. This paper proposes a general approach to include drivability constraints in the EMS of powertrains provided with discrete automated transmissions. As a first step, determinist dynamic programming is used to compute the optimal solution of the resulting optimization problem. This optimal solution is then used to assess the performance of the proposed online strategy, which is based on the equivalent consumption minimization strategy. Finally, simulation results are completed by a discussion of experimental results, as the proposed online strategy has been implemented in a prototype vehicle designed at Renault and tested on a dynamometric bench.

Implementation of an Energy Management Strategy for Hybrid Electric Vehicles Including Drivability Constraints

Blending gear shift strategy design and comparison study for a battery electric city bus with AMT

In the present work, the Adaptive-Weight Genetic Algorithm was employed in order to determine the gear shifting strategies that allow an automobile to work in the best compromise among fuel consumption, engine emissions, and vehicle performance. For the assessment of each of the three objective functions, a simulation model based on engine data and on the well-established equations of the longitudinal dynamics was developed. The driving cycle chosen for the calculations was the FTP-75, which takes into account both cold and hot starts, meaning that the transient operation during the warm-up of the catalyst is also considered.

Gear shifting multi-objective optimization to improve vehicle performance, fuel consumption, and engine emissions

This paper aims at designing optimal gear shift strategies for conventional passenger vehicles equipped with discrete ratio transmissions. In order to study quantitatively an optimal trade-off between the fuel economy and the driveability, the vehicle driveability is addressed in a fuel-optimal gear shift algorithm based on dynamic programming by three methods: method 1, weighted inverse of power reserve; method 2, constant power reserve; method 3, variable power reserve. Furthermore, another method based on stochastic dynamic programming is proposed to derive an optimal gear shift strategy over a number of driving cycles in an average sense, hence taking into account the vehicle driveability. In contrast with the dynamic-programming-based strategy, the obtained gear shift strategy based on stochastic dynamic programming is real time implementable. A comparative analysis of all proposed gear shift methods is given in terms of the improvements in the fuel economy and the driveability. The variable-power-re...

Jose A. Colin Navarrete

Papers

Optimal gear shift strategies for fuel economy and driveability