Battery Protective Electric Vehicle Charging Management in Renewable Energy System

The adoption of grid-connected electric vehicles (GEVs) brings a bright prospect for promoting renewable energy. An efficient vehicle-to-grid (V2G) scheduling scheme that can deal with renewable energy volatility and protect vehicle batteries from fast aging is indispensable to enable this benefit. This article develops a novel V2G scheduling method for consuming local renewable energy in microgrids by using a mixed learning framework. It is the first attempt to integrate battery protective targets in GEVs charging management in renewable energy systems. Battery safeguard strategies are derived via an offline soft-run scheduling process, where V2G management is modeled as a constrained optimization problem based on estimated microgrid and GEVs states. Meanwhile, an online V2G regulator is built to facilitate the real-time scheduling of GEVs' charging. The extreme learning machine (ELM) algorithm is used to train the established online regulator by learning rules from soft-run strategies. The online charging coordination of GEVs is realized by the ELM regulator based on real-time sampled microgrid frequency. The effectiveness of the developed models is verified on a U.K. microgrid with actual energy generation and consumption data. This article can effectively enable V2G to promote local renewable energy with battery aging mitigated, thus economically benefiting EV owns and microgrid operators, and facilitating decarbonization at low costs.</p

Linearizing Battery Degradation for Health-aware Vehicle Energy Management

The utilization of battery energy storage systems (BESS) in vehicle-to-grid (V2G) and plug-in hybrid electric vehicles (PHEVs) benefits the realization of net-zero in the energy-transportation nexus. Since BESS represents a substantial part of vehicle total costs, the mitigation of battery degradation should be factored into energy management strategies. This paper proposes a two-stage BESS aging quantification and health-aware energy management method for reducing vehicle battery aging costs. In the first stage, a battery aging state calibration model is established by analyzing the impact of cycles with various Crates and depth of discharges based on a semi-empirical method. The model is further linearized by learning the mapping relationship between aging features and battery life loss with a linear-in-the-parameter supervised learning method. In the second stage, with the linear battery life loss quantification model, a neural hybrid optimization-based energy management method is developed for mitigating vehicle BESS aging. The battery aging cost function is formulated as a linear combination of system states, which simplifies model solving and reduces computation cost. The case studies in an aggregated EVs peak-shaving scenario and a PHEV with an engine-battery hybrid powertrain demonstrate the effectiveness of the developed method in reducing battery aging costs and improving vehicle total economy. This work provides a practical solution to hedge vehicle battery degradation costs and will further promote decarbonization in the energy-transportation nexus.</p

Hybrid Power System Topology and Energy Management Scheme Design for Hydrogen-Powered Aircraft

The electrification of the aviation industry is a major challenge to realizing net-zero in the global energy sector. Fuel cell (FC) hybrid electric aircraft (FCHEV) demonstrate remarkable competitiveness in terms of cruise range and total economy. However, the process of simply hybridizing different power supplies together does not lead to an improvement in the aircraft economy, since a carefully designed power system topology and energy management scheme are also necessary to realize the full benefit of FCHEV. This paper provides a new approach towards the configuration of the optimal power system and proposes a novel energy management scheme for FCHEA. Firstly, four different topologies of aircraft power systems are designed to facilitate flexible power flow control and energy management. Then, an equivalent model of aircraft hydrogen consumption is formulated by analyzing the FC efficiency, FC aging, and BESS aging. Using the newly established model, the performance of aircraft can be quantitatively evaluated in detail to guide FCHEA design. The optimal aircraft energy management is realized by establishing a mathematical optimization model with the reduction of hydrogen consumption and aging costs as objectives. An experimental aircraft, NASA X-57 Maxwell, is used to provide a detailed performance evaluation of different power system topologies and validate the effectiveness of the energy management scheme. The new approach represents a guide for future power system design and energy management of electric aircraft.</p

Factoring Electrochemical and Full-Lifecycle Aging Modes of Battery Participating in Energy and Transportation Systems

Transportation electrification emerges as a pivotal strategy to realize deep decarbonization for many countries, and the central part of this is battery. However, a key challenge often overlooked is the impact of battery aging on the economy and longevity of electric vehicles (EVs). To address this issue, the paper proposes a novel battery full-life degradation (FLD) model and energy management framework that substantially improves the overall economic efficiency of Battery Energy Storage Systems (BESS). In the first stage, battery electrochemical aging features are modeled by learning cell fading rate under various healthy states, capitalized on the Stanford experimental open dataset. Accordingly, a lifecycle degradation model is then developed considering various operational conditions and aging stages to quantitatively assess the effects of depth of discharge, C-rate, state of health, and state of charge. In the second stage, battery electrochemical aging features are integrated into vehicle energy management so that batteries under different fading rates can be flexibly utilized during whole lifecycles. The proposed methods are validated on a practical UK distribution network and a hybrid vehicles hardware-in-the-loop platform. With the proposed methods, EV users can make informed decisions to optimize energy usage and prolong the lifespan of vehicle BESS, thereby fostering a more sustainable and efficient transportation infrastructure.</p

Factoring Electrochemical and Full-Lifecycle Aging Modes of Battery Participating in Energy and Transportation Systems

Transportation electrification emerges as a pivotal strategy to realize deep decarbonization for many countries, and the central part of this is battery. However, a key challenge often overlooked is the impact of battery aging on the economy and longevity of electric vehicles (EVs). To address this issue, the paper proposes a novel battery full-life degradation (FLD) model and energy management framework that substantially improves the overall economic efficiency of Battery Energy Storage Systems (BESS). In the first stage, battery electrochemical aging features are modeled by learning cell fading rate under various healthy states, capitalized on the Stanford experimental open dataset. Accordingly, a lifecycle degradation model is then developed considering various operational conditions and aging stages to quantitatively assess the effects of depth of discharge, C-rate, state of health, and state of charge. In the second stage, battery electrochemical aging features are integrated into vehicle energy management so that batteries under different fading rates can be flexibly utilized during whole lifecycles. The proposed methods are validated on a practical UK distribution network and a hybrid vehicles hardware-in-the-loop platform. With the proposed methods, EV users can make informed decisions to optimize energy usage and prolong the lifespan of vehicle BESS, thereby fostering a more sustainable and efficient transportation infrastructure.</p

Linearizing battery degradation for health-aware vehicle energy management

The utilization of battery energy storage systems (BESS) in vehicle-to-grid (V2G) and plug-in hybrid electric vehicles (PHEVs) benefits the realization of net-zero in the energy-transportation nexus. Since BESS represents a substantial part of vehicle total costs, the mitigation of battery degradation should be factored into energy management strategies. This paper proposes a two-stage BESS aging quantification and health-aware energy management method for reducing vehicle battery aging costs. In the first stage, a battery aging state calibration model is established by analyzing the impact of cycles with various Crates and depth of discharges based on a semi-empirical method. The model is further linearized by learning the mapping relationship between aging features and battery life loss with a linear-in-the-parameter supervised learning method. In the second stage, with the linear battery life loss quantification model, a neural hybrid optimization-based energy management method is developed for mitigating vehicle BESS aging. The battery aging cost function is formulated as a linear combination of system states, which simplifies model solving and reduces computation cost. The case studies in an aggregated EVs peak-shaving scenario and a PHEV with an engine-battery hybrid powertrain demonstrate the effectiveness of the developed method in reducing battery aging costs and improving vehicle total economy. This work provides a practical solution to hedge vehicle battery degradation costs and will further promote decarbonization in the energy-transportation nexus