Neural network based power management of hydraulic hybrid vehicles


  • Michael Sprengel Maha Fluid Power Research Center, Purdue University, West Lafayette, IN, USA
  • Monika Ivantysynova Maha Fluid Power Research Center, Purdue University, West Lafayette, IN, USA



dynamic programming, neural network, power management, Hydraulic hybrid


Effective power management is key to maximizing the performance and efficiency of hydraulic hybrid powertrains. However, the strong influence of future driving events on the optimal control policy limits the effectiveness of many approaches investigated to date. To address this issue the authors have proposed and investigated a novel power management controller that aims to predict online the accumulator’s near optimal state trajectory. It is demonstrated in this paper that if the optimal accumulator state trajectory is known, then an implementable control scheme can achieve near globally optimal fuel efficiency. Controller development began by optimally controlling a series hybrid over a representative drive cycle using Dynamic Programming (DP). A Neural Network (NN) was then trained to reproduce the DP optimal accumulator pressure trajectory based on the vehicle’s velocity over the previous thirty seconds. In this way the NN generalized the relationship between vehicle velocity and accumulator pressure. The NN power management controller’s performance was then evaluated on a hardware-in-the-loop transmission dynamometer using untrained drive cycles to demonstrate the generality of the proposed approach. During these untrained evaluation cycles the NN controller was able to decrease average fuel consumption by 25.8% when compared to a baseline constant pressure control strategy.


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Author Biographies

Michael Sprengel, Maha Fluid Power Research Center, Purdue University, West Lafayette, IN, USA

Michael Sprengel was born on 13 May 1987 in Cape Girardeau, Missouri (USA). He received his BS in Mechanical Engineering at the Missouri University of Science and Technology in 2010, his MS in Mechanical Engineering from Purdue University in 2013, and his PhD in Engineering from Purdue University in 2015. Currently he, is employed as an R&D engineer/analyst at Czero in Fort Collins, CO. His research interests include the design, simulation, control, and optimization of novel energy efficient hydraulic hybrid systems for on-road and off-highway applications.

Monika Ivantysynova, Maha Fluid Power Research Center, Purdue University, West Lafayette, IN, USA

Monika Ivantysynova was born on 11 December 1955 in Polenz (Germany). She received her MSc degree in Mechanical Engineering and her PhD degree in Fluid Power from the Slovak Technical University of Bratislava, Czechoslovakia. After 7 years in fluid power industry she returned to university. In April 1996 she received a Professorship in fluid power & control at the University of Duisburg (Germany). From 1999 until August 2004 she was Professor of Mechatronic Systems at the Technical University of Hamburg-Harburg. Since August 2004 she is Professor at Purdue University, USA. Her main research areas are energy saving actuator technology and model based optimization of displacement machines as well as modelling, simulation and testing of fluid power systems. Besides the book ‘Hydrostatic Pumps and Motors’ published in German and English, she has published more than 80 papers in technical journals and at international conferences.


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