Holistic Management of Energy Storage System for Electric Vehicles

While electric vehicles (EVs) have recently gained popularity owing to their economic and environmental benefits, they have not yet dominated conventional combustion-engine vehicles in the market. This is due mainly to their short driving range, high cost and/or quick battery performance degradation. One way to mitigate these shortcomings is to optimize the driving range and the degradation rate with a more efficient battery management system (BMS). This dissertation explores how a more efficient BMS can extend EVs' driving range during their warranty periods. Without changing the battery capacity/size, the driving range and the degradation rate can be optimized by adaptively regulating main operational conditions: battery ambient temperature (T), the amount of transferred battery energy, discharge/charge current (I), and the range of operating voltage (min/max V). To this end, we build a real-time adaptive BMS from a cyber-physical system (CPS) perspective. This adaptive BMS calculates target operation conditions (T, I, min/max V) based on: (a) a battery performance model that captures the effects of operational conditions on the degradation rate and the driving range; (b) a real-time battery power predictor; and (c) a temperature and discharge/charge current scheduler to determine target battery operation conditions that guarantee the warranty period and maximize the driving range. Physical components of the CPS actuate battery control knobs to achieve the target operational conditions scheduled by the batteries cyber components of CPS. There are two subcomponents for each condition (T, I): (d) a battery thermal management system and (e) a battery discharge/charge current management system that consists of algorithms and hardware platforms for each sub-system. This dissertation demonstrates that a more efficient real-time BMS can provide EVs with necessary energy for the specified period of time while slowing down performance degradation. Our proposed BMS adjusts temperature and discharge/charge current in real time, considering battery power requirements and behavior patterns, so as to maximize the battery performance for all battery types and drivers. It offers valuable insight into both current and future energy storage systems, providing more adaptability and practicality for various mobile applications such as unmanned aerial vehicles (UAV) and cellular phones with new types of energy storages.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143920/1/kimsun_1.pd

Real-time Optimal Battery Thermal Management System Controller for Electric and Plug-in Hybrid Electric Vehicles

The objective of this thesis is to propose a real-time model predictive control (MPC) scheme for the battery thermal management system (BTMS) of given plug-in hybrid electric and electric vehicles (PHEV/EVs). Although BTMS control in its basic form can be well represented by a reference tracking problem, there exists only little research in the literature taking such an approach. Due to the importance of a prediction component in thermal systems, here the BTMS controller has been designed based on MPC theory to address this gap in the literature. Application of the controller to the baseline vehicles is then examined by several simulations with di erent optimization algorithms. By comparing the results of the predictive controller with those of the standard rulebased (RB) controller over a variety of driving scenarios, it is observed that the predictive controller signi cantly reduces the power consumption and provides a better tracking behaviour. Integrating trip prediction into the control algorithm is particularly important in cases such as aggressive driving cycles and highly variable road-grades, where the standard BTMS scheme does not perform as e ectively due to the load current pro le. Moreover, based on the simulation results, the designed controller is observed to have a turnaround time between 10 s to 1 ms, and is thus applicable to the real-time automotive systems. Prosperity of the proposed BTMS control methodology paves the way for the use of model-based (MB) thermal management techniques, not only in futur

Urban and extra-urban hybrid vehicles: a technological review

Pollution derived from transportation systems is a worldwide, timelier issue than ever. The abatement actions of harmful substances in the air are on the agenda and they are necessary today to safeguard our welfare and that of the planet. Environmental pollution in large cities is approximately 20% due to the transportation system. In addition, private traffic contributes greatly to city pollution. Further, “vehicle operating life” is most often exceeded and vehicle emissions do not comply with European antipollution standards. It becomes mandatory to find a solution that respects the environment and, realize an appropriate transportation service to the customers. New technologies related to hybrid –electric engines are making great strides in reducing emissions, and the funds allocated by public authorities should be addressed. In addition, the use (implementation) of new technologies is also convenient from an economic point of view. In fact, by implementing the use of hybrid vehicles, fuel consumption can be reduced. The different hybrid configurations presented refer to such a series architecture, developed by the researchers and Research and Development groups. Regarding energy flows, different strategy logic or vehicle management units have been illustrated. Various configurations and vehicles were studied by simulating different driving cycles, both European approval and homologation and customer ones (typically municipal and university). The simulations have provided guidance on the optimal proposed configuration and information on the component to be used

Least costly energy management for series hybrid electric vehicles

Energy management of plug-in Hybrid Electric Vehicles (HEVs) has different challenges from non-plug-in HEVs, due to bigger batteries and grid recharging. Instead of tackling it to pursue energetic efficiency, an approach minimizing the driving cost incurred by the user - the combined costs of fuel, grid energy and battery degradation - is here proposed. A real-time approximation of the resulting optimal policy is then provided, as well as some analytic insight into its dependence on the system parameters. The advantages of the proposed formulation and the effectiveness of the real-time strategy are shown by means of a thorough simulation campaign

Nonlinear model predictive control for thermal management in plug-in hybrid electric vehicles

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A nonlinear model predictive control (NMPC) for the thermal management (TM) of Plug-in Hybrid Electric Vehicles (PHEVs) is presented. TM in PHEVs is crucial to ensure good components performance and durability in all possible climate scenarios. A drawback of accurate TM solutions is the higher electrical consumption due to the increasing number of low voltage (LV) actuators used in the cooling circuits. Hence, more complex control strategies are needed for minimizing components thermal stress and at the same time electrical consumption. In this context, NMPC arises as a powerful method for achieving multiple objectives in Multiple input- Multiple output systems. This paper proposes an NMPC for the TM of the High Voltage (HV) battery and the power electronics (PE) cooling circuit in a PHEV. It distinguishes itself from the previously NMPC reported methods in the automotive sector by the complexity of its controlled plant which is highly nonlinear and controlled by numerous variables. The implemented model of the plant, which is based on experimental data and multi- domain physical equations, has been validated using six different driving cycles logged in a real vehicle, obtaining a maximum error, in comparison with the real temperatures, of 2C. For one of the six cycles, an NMPC software-in-the loop (SIL) is presented, where the models inside the controller and for the controlled plant are the same. This simulation is compared to the finite-state machine-based strategy performed in the real vehicle. The results show that NMPC keeps the battery at healthier temperatures and in addition reduces the cooling electrical consumption by more than 5%. In terms of the objective function, an accumulated and weighted sum of the two goals, this improvement amounts 30%. Finally, the online SIL presented in this paper, suggests that the used optimizer is fast enough for a future implementation in the vehicle.Accepted versio

A lumped thermal model of lithium-ion battery cells considering radiative heat transfer

Thermal management plays a critical role in battery operations to improve safety and prolong battery life, especially in high power applications such as electric vehicles. A lumped parameter (LP) battery thermal model (BTM) is usually preferred for real-time thermal management due to its simple structure and ease of implementation. Considering the time-varying model parameters (e.g., the varying convective heat dissipation coefficient under different cooling conditions), an online parameter estimation scheme is needed to improve modelling accuracy. In this paper, a new formulation of adaptive LP BTM is proposed. Unlike the conventional LP BTMs that only consider convection heat transfer, the radiative heat transfer is also considered in the proposed model to better approximate the physical heat dissipation process, which leads to an improved modelling accuracy. On the other hand, the radiative heat transfer introduces nonlinearity to the BTM and poses challenge to online parameter estimation. To tackle this problem, the simplified refined instrumental variable approach is proposed for real-time parameter estimation by reformulating the nonlinear model equations into a linear-in-the-parameter manner. Finally, test data are collected using a Li ion battery. The experimental results have verified the accuracy of the proposed BTM and the effectiveness of the proposed online parameter estimation algorithm