Lithium-ion batteries are widely used in EVs and renewable energies. To determine its current state and predict remaining life, sophisticated evaluation of battery degradation is needed. A battery lifetime prediction method using discharge curve analysis (DCA) is one feasible option. DCA based ageing model determines battery degradation by optimization of fitting parameters subject to local minimum solution. However, with battery ageing and data errors, model optimization gives multiple local minimum solutions, and thus prediction accuracy reduces. Conventional technology reported prediction accuracy of 10% and prediction time of 10 hours. In this research, we have developed an automated parameter optimization algorithm of DCA ageing model which finds multiple local minimum solutions and select the best lifetime prediction function. Algorithm determines parameter’s useful search spaces and performs optimization for multiple ranges by dividing these search spaces to locate multiple local minimum solutions. The combination sets of these solutions according to calendar and cycle life are fitted to power functions and one with lowest normalized error is considered for lifetime prediction. It is implemented in Python and verified for various battery calendar and cycle test data. It was found to maintain 5% prediction accuracy and execution time within an hour.

This paper presents a new way of formalizing the coalition structure generation problem (CSG) so that we can apply constraint optimization techniques to it. Forming effective coalitions is a major research challenge in AI and multi-agent systems. CSG involves partitioning a set of agents into coalitions to maximize social surplus. Traditionally, the input of the CSG problem is a black-box function called a characteristic function, which takes a coalition as input and returns the value of the coalition. As a result, applying constraint optimization techniques to this problem has been infeasible. However, characteristic functions that appear in practice often can be represented concisely by a set of rules, rather than treating the function as a black box. Then we can solve the CSG problem more efficiently by directly applying constraint optimization techniques to this compact representation. We present new formalizations of the CSG problem by utilizing recently developed compact representation schemes for characteristic functions. We first characterize the complexity of CSG under these representation schemes. In this context, the complexity is driven more by the number of rules than by the number of agents. As an initial step toward developing efficient constraint optimization algorithms for solving the CSG problem, we also develop mixed integer programming formulations and show that an off-the-shelf optimization package can perform reasonably well.

We consider a school choice program where minimum quotas are imposed for each school, i.e., a school must be assigned at least a certain number of students to operate. We require that the obtained matching must respect the initial endowments, i.e., each student must be assigned to a school that is at least as good as her initial endowment school. Although minimum quotas are relevant in school choice programs and strategy-proofness is important to many policymakers, few existing mechanisms simultaneously achieve both. One difficulty is that no strategy-proof mechanism exists that is both efficient and fair under the presence of minimum quotas. Furthermore, existing mechanisms require that all students consider all schools acceptable to obtain a feasible matching that respects minimum quotas. This assumption is unrealistic in a school choice program. We consider the environment where a student considers her initial endowment school acceptable and the initial endowments satisfy all the minimum quotas. We develop two strategy-proof mechanisms. One mechanism, which we call the Top Trading Cycles among Representatives with Supplementary Seats (TTCR-SS), is based on the Top Trading Cycles (TTC) mechanism and is significantly extended to handle the supplementary seats of schools while respecting minimum quotas. TTCR-SS is Pareto efficient. The other mechanism, which we call Priority List-based Deferred Acceptance with Minimum Quotas (PLDA-MQ), is based on the Deferred Acceptance (DA) mechanism. PLDA-MQ is fair, satisfies a concept called Priority List-based (PL-) stability, and obtains the student-optimal matching within all PL-stable matchings. Our simulation results show that our new mechanisms are significantly better than simple extensions of the existing mechanisms.

Forming effective coalitions is a major research challenge in AI and multi-agent systems (MAS). Coalition Structure generation (CSG) involves partitioning a set of agents into coalitions so that social surplus (the sum of the rewards of all coalitions) is maximized. A partition is called a Coalition Structure (CS). In traditional works, the value of a coalition is given by a black box function called a characteristic function. In this paper, we propose a novel formalization of CSG, i.e., we assume the value of a characteristic function is given by an optimal solution of a distributed constraint optimization problem (DCOP) among the agents of a coalition. A DCOP is a popular approach for modeling cooperative agents, since it is quite general and can formalize various application problems in MAS. At first glance, one might assume that the computational costs required in this approach would be too expensive, since we need to solve an NP-hard problem just to obtain the value of a single coalition. To optimally solve a CSG, we might need to solve n-th power of 2 DCOP problem instances, where n is the number of agents. However, quite surprisingly, we show that an approximation algorithm, whose computational cost is about the same as solving just one DCOP, can find a CS with quality guarantees. More specifically, we develop an algorithm with parameter k that can find a CS whose social surplus is at least max(k/(w*+1), 2k/n) of the optimal CS, where w* is the tree width of a constraint graph. When k=1, the complexity of this algorithm is about the same as solving just one DCOP. These results illustrate that the locality of interactions among agents, which is explicitly modeled in the DCOP formalization, is quite useful in developing an efficient CSG algorithm with quality guarantees.