Session Talks

Talk 1: "Reconfigurable Battery Systems" by Dr. Swaminathan Narayanaswamy, Technical University of Munich

Short Abstract: Recently, applications using high power battery packs such as Electric Vehicles (EVs), Hybrid Electric Vehicles (HEVs) and stationary Electrical Energy Storage (EES) systems are gaining more importance. Battery packs for these applications typically consist of several number of individual cells connected in parallel and in series in order to obtain a higher capacity and voltage, respectively. State-of-the-art approaches focus on a fixed electrical connection scheme between the individual cells to form a high power battery pack. In contrast to this, reconfigurable battery packs that dynamically modify the electrical connection scheme of the individual cells are an emerging alternative. Such dynamic reconfiguration changes the number of cells connected in parallel or in series at real-time depending upon the changes in the load characteristics and the characteristics of the individual cells, in order to provide high energy efficiency, long runtime, fault protection and low battery degradation. Several battery reconfiguration circuit architectures and corresponding algorithms for controlling these underlying circuit architectures have been proposed in the literature. In this talk, an overview of the existing battery reconfiguration techniques including the different circuit architectures, their modeling approaches and software control algorithms will be provided from a real-time perspective. In addition, open challenges in the domain of real-time battery reconfiguration will be explained in detail.

Talk 2: "Optimized Charge and Drive Management for EVs" by Prof. Mohammad Al Faruque, University of California, Irvine

Short Abstract: The assumption of future proliferates the usage of the Electric Vehicles (EV) drives the research to come out new optimized algorithms which can increase the battery life considering all the important factors. We have proposed several algorithms in this regard, such as Optimized Charge and Drive Management (OCDM), Optimized Thermal and Energy Management (OTEM), Adaptive and Cooperative Quality-Aware (ACQUA), those consider important factors such as driving pattern, climate conditions, the temperature of the battery cell, road conditions, HVAC, driving range, etc. to come up with an optimized solution to increase the battery lifetime. All these algorithms use the sensor fused data which are coming from the different sensors such as battery charge sensing device, temperature sensing device, accelerometer, LIDAR, etc. These sensors fused data may deviate from the actual measurements due to the presence of unavoidable noise, aging of the sensing device, unexpected loss of signal, etc. and these unseen scenarios can easily restrain the above algorithms not to reach in the optimum state. To increase reliability, these algorithms should consider the added noise in the sensor fused data and try to come to the optimum state by not hurting the driving comfort. In our very recent work, we propose to apply Extended Kalman Filter with these algorithms to handle the unpredictability of the sensor fused data and make these algorithms more reliable and noise immune. During the talk, we will present these algorithmic advances for optimized charge and EV management.

Talk 3: "Managing EV Charging Stations" by Prof. Sangyoung Park, Technical University of Berlin

Short Abstract: EVs suffer from long charging times and short-drive ranges limiting EV usage to daily short-range commuting rather than general purpose use. Among the candidates for EV charging infrastructures, the public EV charging station architecture has benefits in that it allows an efficient investment of costly equipment, and a long-range travel which requires multiple charging cycles. This paper focuses on an architecture comprising PV panels, an energy storage system (ESS) and multiple fast-DC charging posts. Systematically deriving the optimal planning, i.e. determining the optimal sizes of the components, is a complicated problem as the EV charging station operations and planning are intertwined. In this paper, we derive EV charging station operation policies by formulating an average reward Markov decision process (MDP) maximization problem to synthesize controllers that maximize the operating income. Then, the controllers are used to evaluate the operating income for EV charging station planning. For efficient exploration of the design space, we perform a mixed search-based technique combining the sequential quadratic programming (SQP) and the greedy algorithm. There will be significant gain in terms of long-term operating cost when the costs of ESS and PV panels continue to reduce in the future. Our solution framework is a helpful tool for such reasoning and finding the optimal planning and operation policy of public EV charging stations.