Equivalent consumption minimization strategy for plug-in hybrid electric vehicles

Abstract

Greenhouse gases are the primary cause of climate change, because greenhouse gases prevent the turn back of the sunlight to the atmosphere and due to this reason, most of the heat that coming from the sun is absorbed by earth's surface and this heat warms the low atmosphere and earth's surface. Due to biggest cause of greenhouse gasses is carbon dioxide and main reason for carbon dioxide formation is transportation with one third rate, governments are taking measures to minimize carbon dioxide gas emissions in order to slow global warming especially for the transportation. Due to increasing vehicle number in the world, tight carbon dioxide emission regulations were introduced in 2021. These new standards present significant legal hurdles to the vehicle industry in terms of lowering exhaust emissions below a certain value. At that point, hybrid electric vehicles play a critical role to achieving this goal for OEMs. Most of the vehicle manufacturer must invest hybrid technologies to be able to sell their products to the market. Hybrid electric vehicles have an electric motor and battery package in addition to a conventional internal combustion engine, which is utilized to reduce fuel consumption and consequently carbon dioxide emissions by employing the electric engine in areas where the internal combustion engine efficiency is too low. Generally, in the urban driving where the start and stop activations are too much, electric motors can handle the start the internal combustion engine and provide traction for overcome the necessary inertial forces. They also provide additional torque in transient maneuvers where fast torque build is necessary for better acceleration and allow pure electric drive where just electric motor is used as a traction source depending on the battery and electric motor power capacity. They can be categorized into four classes based on components and degree of hybridization which consist of micro hybrid, mild hybrid, full hybrid (FHEV) and plug-in hybrid (PHEV). As main topologies, they are splitted 3 main parts depending on the power flow in the powertrain of vehicle: series hybrid, parallel hybrid and power-split hybrid. In terms of operational strategy on the hybrid vehicle, for getting the best fuel economy which means lowest carbon dioxide, it's critical to run the internal combustion engine and electric motor as efficiently as possible. As a result, the topic of energy management and energy optimization have become a major focus in the automotive industry. There are two main types of energy management strategies; rule-based and optimization based. Rule based techniques use heuristics methods, as well as previous experiences and vehicle data or test data, to build a set of rules for managing an operating strategy. On the optimization-based side, there are main 2 categories exist: global optimization and real time optimization. If the driving cycle characteristics are known, global optimization methods give satisfying results for energy management. Global optimization is an area of applied mathematics and numerical analysis that searches to locate the global minimum and maximum of a function and it is commonly referred to as a minimization problem. Dynamic programming and genetic algorithm are the most common global optimization methods used for energy management side. But in the real life, due to it is hard to detect or understand the cycle characteristic previously, generally real time optimization methods are preferred. The most utilized instantaneous optimization method for this purpose is the equivalent consumption minimization strategy (ECMS). The main logic behind ECMS is to convert energy consumption of electric equipment into fuel consumption using an equivalent factor (EF), which is then added to the engine's real fuel consumption to produce the equivalent total virtual fuel consumption at any given time. The minimum energy distribution of HEVs is solved using the equivalent fuel consumption as the goal function. Due to ECMS uses a static equivalent for the energy management systems, it is so crucial to select the correct factor in sensible way, otherwise, either excessive battery consumption is seen or ICE works too much and causes too high fuel consumption. Especially for the plug-in hybrid vehicles which have high voltage battery package with the high battery capacity, it is critical to keep the state of charge above a certain threshold for the battery health. Although ECMS uses a static value, with the correct selection of equivalent factor, it gives satisfying results in terms of decreasing the fuel consumption and carbon dioxide. In this thesis, a P2 plug-in hybrid electric vehicle model was created in MATLAB Simulink tool including both internal combustion engine and electric motor with high voltage battery system. According to the regulation for plug-in hybrid electric vehicle, charge depleting and charge sustaining tests were performed on the vehicle model. For the energy management side, ECMS algorithm was implemented to plug-in hybrid electric vehicle model as a controller for the torque distribution between ICE and EM. Then the plug-in hybrid electric vehicle model has been converted conventional internal combustion engine model taking into the account the weight decreased origined from high voltage battery and electrified powertrain to be fair for fuel comparison. Plug-in hybrid model with the ECMS energy management system has been analyzed in WLTP Class 3b and the results showed that, with the optimum static equivalent factor selection, fuel consumption has been decreased about 29.10%. And also, in charge depleting test, in 6 times back-to-back WLTP tests, 98.88 km electric range was obtained where only EM has been used as traction source. Additionally, after the charge depleting test, during the confirmation cycle where the electric drive is switched off and ICE is activated, charge state of HV battery was kept as expected over a certain threshold. Details of ECMS code is given in the appendices section.