Modelling and control of hybrid electric vehicles (a comprehensive review)

The gradual decline in global oil reserves and presence of ever so stringent emissions rules around the world, have created an urgent need for the production of automobiles with improved fuel economy. HEVs (hybrid electric vehicles) have proved a viable option to guarantying improved fuel economy and reduced emissions.The fuel consumption benefits which can be realised when utilising HEV architecture are dependent on how much braking energy is regenerated, and how well the regenerated energy is utilized. The challenge in developing an HEV control strategy lies in the satisfaction of often conflicting control constraints involving fuel consumption, emissions and driveability without over-depleting the battery state of charge at the end of the defined driving cycle.To this effect, a number of power management strategies have been proposed in literature. This paper presents a comprehensive review of these literatures, focusing primarily on contributions in the aspect of parallel hybrid electric vehicle modelling and control. As part of this treatise, exploitable research gaps are also identified. This paper prides itself as a comprehensive reference for researchers in the field of hybrid electric vehicle development, control and optimization

Supervisory Control Optimization for a Series Hybrid Electric Vehicle with Consideration of Battery Thermal Management and Aging

This dissertation integrates battery thermal management and aging into the supervisory control optimization for a heavy-duty series hybrid electric vehicle (HEV). The framework for multi-objective optimization relies on novel implementation of the Dynamic Programing algorithm, and predictive models of critical phenomena. Electrochemistry based battery aging model is integrated into the framework to assesses the battery aging rate by considering instantaneous lithium ion (Li+) surface concentration rather than average concentration. This creates a large state-action space. Therefore, the computational effort required to solve a Deterministic or Stochastic Dynamic Programming becomes prohibitively intense, and a neuro-dynamic programming approach is proposed to remove the ‘curse of dimensionality’ in classical dynamic programming. First, unified simulation framework is developed for in-depth studies of series HEV system. The integration of a refrigerant system model enables prediction of energy use for cooling the battery pack. Side reaction, electrolyte decomposition, is considered as the main aging mechanism of LiFePO4/Graphite battery, and an electrochemical model is integrated to predict side reaction rate and the resulting fading of capacity and power. An approximate analytical solution is used to solve the partial difference equations (PDEs) for Li+ diffusion. Comparing with finite difference method, it largely reduces the number of states with only a slight penalty on prediction accuracy. This improves computational efficiency, and enables inclusion of the electrochemistry based aging model in the power management optimization framework. Next, a stochastic dynamic programming (SDP) approach is applied to the optimization of supervisory control. Auxiliary cooling power is included in addition to vehicle propulsion. Two objectives, fuel economy and battery life, are optimized by weighted sum method. To reduce the computation load, a simplified battery aging model coupled with equivalent circuit model is used in SDP optimization; Li+ diffusion dynamics are disregarded, and surface concentration is represented by the average concentration. This reduces the system state number to four with two control inputs. A real-time implementable strategy is generated and embedded into the supervisory controller. The result shows that SDP strategy can improve fuel economy and battery life simultaneously, comparing with Thermostatic SOC strategy. Further, the tradeoff between fuel consumption and active Li+ loss is studied under different battery temperature. Finally, the accuracy of battery aging model for optimization is improved by adding Li+ diffusion dynamics. This increases the number of states and brings challenges to classical dynamic programming algorithms. Hence, a neuro-dynamic programming (NDP) approach is proposed for the problem with large state-action space. It combines the idea of functional approximation and temporal difference learning with dynamic programming; in that case the computation load increases linearly with the number of parameters in the approximate function, rather than exponentially with state space. The result shows that ability of NDP to solve the complex control optimization problem reliably and efficiently. The battery-aging conscientious strategy generated by NDP optimization framework further improves battery life by 3.8% without penalty on fuel economy, compared to SDP strategy. Improvements of battery life compared to the heuristic strategy are much larger, on the order of 65%. This leads to progressively larger fuel economy gains over time

Self-Learning Neural controller for Hybrid Power Management using Neuro-Dynamic Programming

A supervisory controller strategy for a hybrid vehicle coordinates the operation of the two power sources onboard of a vehicle to maximize objectives like fuel economy. In the past, various control strategies have been developed using heuristics as well as optimal control theory. The Stochastic Dynamic Programming (SDP) has been previously applied to determine implementable optimal control policies for discrete time dynamic systems whose states evolve according to given transition probabilities. However, the approach is constrained by the curse of dimensionality, i.e. an exponential increase in computational effort with increase in system state space, faced by dynamic programming based algorithms. This paper proposes a novel approach capable of overcoming the curse of dimensionality and solving policy optimization for a system with very large design state space. We propose developing a supervisory controller for hybrid vehicles based on the principles of reinforcement learning and neuro-dynamic programming, whereby the cost-to-go function is approximated using a neural network. The controller learns and improves its performance over time. The simulation results obtained for a series hydraulic hybrid vehicle over a driving schedule demonstrate the effectiveness of the proposed technique.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89874/1/draft_01.pd

Optimal Energy Management for a Hybrid Vehicle Using Neuro-Dynamic Programming to Consider Transient Engine Operation

This paper proposes a self-learning approach to develop optimal power management with multiple objectives, e.g. to minimize fuel consumption and transient engine-out NOx and particulate matter emission for a series hydraulic hybrid vehicle. Addressing multiple objectives is particularly relevant in the case of a diesel powered hydraulic hybrid since it has been shown that managing engine transients can significantly reduce real-world emissions. The problem is formulated as an infinite time horizon stochastic sequential decision making/markovian problem. The problem is computationally intractable by conventional Dynamic programming due to large number of states and complex modeling issues. Therefore, the paper proposes an online self-learning neural controller based on the fundamental principles of Neuro-Dynamic Programming (NDP) and reinforcement learning. The controller learns from its interactions with the environment and improves its performance over time. The controller tries to minimize multiple objectives and continues to evolve until a global solution is achieved. The control law is a stationary full state feedback based on 5 states and can be directly implemented. The controller performance is then evaluated in the Engine-in-the-Loop (EIL) facility.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89876/1/draft_01.pd

Intelligent energy management in hybrid electric vehicles

The modelling and simulation approach is employed to develop an intelligent energy management system for hybrid electric vehicles. The aim is to optimize fuel consumption and reduce emissions. An analysis of the role of drivetrain, energy management control strategy and the associated impacts on the fuel consumption with combined wind/drag, slope, rolling, and accessories loads are included.<br /

A state-of-the-art review on torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains

© 2019, Levrotto and Bella. All rights reserved. Electric vehicles are the future of private passenger transportation. However, there are still several technological barriers that hinder the large scale adoption of electric vehicles. In particular, their limited autonomy motivates studies on methods for improving the energy efficiency of electric vehicles so as to make them more attractive to the market. This paper provides a concise review on the current state-of-the-art of torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains (FEVIADs). Starting from the operating principles, which include the "control allocation" problem, the peculiarities of each proposed solution are illustrated. All the existing techniques are categorized based on a selection of parameters deemed relevant to provide a comprehensive overview and understanding of the topic. Finally, future concerns and research perspectives for FEVIAD are discussed

Enhancing Performance of Hybrid Electric Vehicle using Optimized Energy Management Methodology

The fuel consumption and the fuel management strategy (PMS) of the hybrid electric vehicle are closely linked (HEV). In this study, a hybrid power management technique and an adaptive neuro-fuzzy inference (ANFIS) method are established. Artificial intelligence represents a huge improvement in electricity management across different energy sources (AI). The main energy source of the hybrid power supply is a proton exchange membrane fuel cell (PEMFC), while its electrical storage devices are a battery bank and an ultracapacitor. The hybrid electric vehicle's power management strategy (PMS) and fuel consumption are closely related (HEV). In this paper, an adaptive neuro-fuzzy inference and hybrid power management strategy (ANFIS) approach is developed. A significant advance in electricity management across multiple energy sources is artificial intelligence (AI). The proton exchange membrane fuel cell (PEMFC) serves as the primary energy source of the hybrid power supply, and the ultracapacitor and battery bank serve as its electrical storage components

Fuzzy Logic Controller for Parallel Plug-in Hybrid Vehicle

Hybrid electric vehicles combine two methods for propelling a vehicle. In a parallel hybrid vehicle, the two propulsion methods work in parallel to meet the total power demand. Among different combination of power sources, internal combustion engine and electric motor drive system are the most popular because of their availability and controllability. Plug-in hybrid vehicle is the latest version in hybrid vehicle family. In plug-in hybrid vehicle, battery is directly recharged from the electrical power grid and it can be used for a long distance with higher efficiency. Most of the hybrid vehicles on the road are parallel in nature and battery is recharged directly by the engine. If it is possible to convert existing hybrid vehicle into plug-in hybrid vehicle, it will lead to significant improvements in fuel economy and emissions.In this thesis, two fuzzy logic controllers have been developed for the energy management system of the hybrid vehicle. For the first controller, it is assumed that the vehicle will work like a plug-in hybrid vehicle. For the second controller it is assumed that the battery will always recharged by the engine. It is found that with the help of developed fuzzy logic controller, the plug-in hybrid vehicle can run up to 200 miles with high efficiency. Both controllers are developed and their performance is tested on the highly reliable vehicle modeling and simulation software AUTONOMIE. The main objective of developing the controllers is increasing the fuel economy of the vehicle. The results from the both developed controllers are compared with the default controller in AUTONOMIE in order to show performance improvements