Hybrid and electric vehicle (HEV/EV) technology is reasonably mature at this time, with a few million vehicles around in the world, and there is a significant amount of literature in the public domain on this subject. However, there is not enough literature on the application of this technology for off-road vehicles, including construction equipment, other industrial utility vehicles, and nonautomotive applications, such as a locomotive, ships, or airborne vehicles. With this in mind, the author presents here the topic and its current status. In addition, the author discusses the issue related to the decision-making process before the above technology is introduced for any HEV/EV application so that one is assured that the technology will bring benefit if applied for a particular purpose.

This paper presents our research in vehicle energy optimization for a low-cost HEV power system that only allows the control of engine on/off and driving at three different speed limits. We present algorithms for modeling vehicle energy flow and optimization and machine learning of optimal control settings generated by Dynamic Programming on real-world drive cycles, and an intelligent energy controller designed for online energy control. Experimental results show the intelligent controller has the capability of 11% fuel saving.

This paper presents our research in battery State of Charge (SOC) estimation for intelligent battery management. Our research focus is to investigate online dynamic SOC estimation using a combination of Kalman filtering and a neural network. First, we developed a method to model battery hysteresis effects using Extended Kalman Filter (EKF). Secondly, we designed a SOC estimation model, NN-EKF model, that incorporates the estimation made by the EKF into a neural network. The proposed methods have been evaluated using real data acquired from two different batteries, a lithium-ion battery U1-12XP and a NiMH battery with 1.2 V and 3.4 Ah. Our experiments show that our EKF method developed to model battery hysteresis based on separated charge and discharge Open Circuit Voltage (OCV) curves gave the top performances in estimating SOC when compared with other advanced methods. Secondly, the NN-EKF model for SOC estimation gave the best SOC estimation with and without temperature data.

We present our research in optimal power management for a generic vehicle power system that has multiple power sources using machine learning and fuzzy logic. A machine learning algorithm, LOPPS, has been developed to learn about optimal power source combinations with respect to minimum power loss for all possible load requests and various system power states. The results generated by the LOPPS are used to build a fuzzy power controller (FPC). FPC is integrated into a simulation program implemented by using a generic simulation software as indicated in reference [22] and is used to dynamically allocate optimal power sources during online drive. The simulation results generated by FPC show that the proposed machine learning algorithm combined with fuzzy logic is a promising technology for vehicle power management.

Vehicle power management has been an active research area in the past decade, and has intensified recently by the emergence of hybrid electric vehicle technologies. Research has shown that driving style and environment have strong influence over fuel consumption and emissions. In order to incorporate this type of knowledge into vehicle power management, an intelligent system has to be developed to predict the current traffic conditions. This paper presents our research in neural learning for predicting the driving environment such as road types and traffic congestions. We developed a prediction model, an effective set of features to characterize different types of roadways, and a neural network trained for online prediction of roadway types and traffic congestion levels. This prediction model was then used in conjunction with a power management strategy in a conventional (non-hybrid) vehicle. The benefits of having the predicted drive cycle available are demonstrated through simulation.

This paper presents a machine learning approach to the efficient vehicle power management and an intelligent power controller (IPC) that applies the learnt knowledge about the optimal power control parameters specific to specific road types and traffic congestion levels to online vehicle power control. The IPC uses a neural network for online prediction of roadway types and traffic congestion levels. The IPC and the prediction model have been implemented in a conventional (non-hybrid) vehicle model for online vehicle power control in a simulation program. The benefits of the IPC combined with the predicted drive cycle are demonstrated through simulation. Experiment results show that the IPC gives close to optimal performances.