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We propose algorithms to speed up physics-based battery lifetime simulations by one to two orders of magnitude compared to the state-of-the-art. First, we propose a reformulation of the Single Particle Model with side reactions to remove algebraic equations and hence reduce stiffness, with 3x speed-up in simulation time (intra-cycle reformulation). Second, we introduce an algorithm that makes use of the difference between the “fast” timescale of battery cycling and the “slow” timescale of battery degradation by adaptively selecting and simulating representative cycles, skipping other cycles, and hence requires fewer cycle simulations to simulate the entire lifetime (adaptive inter-cycle extrapolation). This algorithm is demonstrated with a specific degradation mechanism but can be applied to various models of aging phenomena. In the particular case study considered, simulations of the entire lifetime are performed in under 5 s. This opens the possibility for much faster and more accurate model development, testing, and comparison with experimental data.

 

Accurate tracking of the internal electrochemical states of lithium-ion battery during cycling enables advanced battery management systems to operate the battery safely and maintain high performance while minimizing battery degradation. To this end, techniques based on voltage measurement have shown promise for estimating the lithium surface concentration of active material particles, which is an important state for avoiding aging mechanisms such as lithium plating. However, methods relying on voltage often lead to large estimation errors when the model parameters change during aging. In this paper, we utilize the in-situ measurement of the battery expansion to augment the voltage and develop an observer to estimate the lithium surface concentration distribution in each electrode particle. We demonstrate that the addition of the expansion signal enables us to correct the negative electrode concentration states in addition to the positive electrode. As a result, compared to a voltage only observer, the proposed observer can successfully recover the surface concentration when the electrodes' stoichiometric window changes, which is a common occurrence under aging by loss of lithium inventory. With a 5% shift in the electrodes' stoichiometric window, the results indicate a reduction in state estimation error for the negative electrode surface concentration. Under this simulated aged condition, the voltage based observer had 9.3% error as compared to the proposed voltage and expansion observer which had 0.1% error in negative electrode surface concentration. 

Studies on eco-driving have mostly taken an energy-centric view and considered driving performance, while less attention has been paid on emissions behavior. This work extends in an experimentally verified way our understanding of the trade-offs among fuel economy, driving aggressiveness, and, especially, emissions in connected automated diesel-powered vehicles. Experiments are performed with a 6.7-L Ford Powerstroke diesel engine, a urea-SCR based NOx aftertreatment system, and a full model for a Ford F250 medium-duty truck in the loop to provide realistic assessment of fuel consumption, tailpipe emissions, and driving style performances. An energy and emissions conscious speed planner is leveraged to follow the traffic. This planner offers flexibility in prioritizing energy or emissions while satisfying user-defined headway constraints, and thus allows exploration of different calibrations in a unified way. Results show how various calibrations of the flexible leader following policy yield 8%–14% decrease in total fuel consumption and 64%–70% decrease in tailpipe emissions compared with a strictly constrained following policy.

 

Li-ion battery internal short circuits are a major safety issue for electric vehicles, and can lead to serious consequences such as battery thermal runaway. An internal short can be caused by mechanical abuse, high temperature, overcharging, and lithium plating. The low impedance or hard internal short circuit is the most dangerous kind. The high internal current flow can lead to battery temperature increase, thermal runaway, and even explosion in a few seconds. Algorithms that can quickly detect such serious events with a high confidence level and which are robust to sensor noise are needed to ensure passenger safety. False positives are also undesirable as many thermal runaway mitigation techniques, such as activating pyrotechnic safety switches, would disable the vehicle. Conventional methods of battery internal short detection, including voltage and surface temperature based algorithms, work well for a single cell. However, these methods are difficult to apply in large scale battery packs with many parallel cells. In this study, we propose a new internal short detection method by using cell swelling information during the early stages of a battery heating caused by an internal short circuit. By measuring cell expansion force, higher confidence level detection can be achieved for an internal short circuit in an electric vehicle scale battery pack. 

While perturbation schemes for vehicle-to-vehicle (V2V) communications can address data privacy concerns, they can significantly compromise the performance of the speed controllers of connected automated vehicles (CAVs) if such controllers rely on the preview information available through V2V in car-following scenarios. This paper presents a robust predictive speed controller for a CAV when preview information is provided through a privacy-guaranteed V2V communication network. This is the first such controller that considers energy and emissions concurrently. The impact of privacy assurance in the communication data is studied, while inter-vehicular distance constraint is guaranteed to be satisfied through a robust design of the predictive controller using a robust control invariant set. The robust optimal speed controller is shown to reduce fuel consumption and emissions successfully while satisfying the constraints even in the presence of perturbations in the V2V communication. Results suggest a need for an integrated design procedure to achieve the best performance under a given level of privacy guarantee and emissions requirements.