Accurate estimation of lithium battery state of charge is very important for ensuring the operation of battery management system, realizing the energy management strategy of electric vehicles, reducing mileage anxiety and promoting the sustainable development of electric vehicles. In this paper, several studies are carried out for state of charge estimation of lithium-ion battery: (1) Aiming at the problem of parameter identification of battery model, an optimal identification method of model parameters based on ant lion optimization algorithm is proposed. (2) An adaptive weighting Cubature particle filter (AWCPF) method is proposed for SOC estimation. The proposed AWCPF method is based on particle filter (PF) algorithm, while the Cubature Kalman filter (CKF) algorithm is utilized to generate the proposal distribution for PF algorithm, which can retrain the particles degradation problem in PF algorithm. To solve the problem that the CKF algorithm is sensitive to noise, comparing with fixed sigma point weights of the conventional CKF, the weights of sigma points are adaptively adjusted based on state and measurement residual vectors. Furthermore, the process noise and measurement noise are estimated iterative. In this paper, experimental verification of different initial values of SOC under various working conditions is carried out. The results show that the proposed AWCPF algorithm based SOC estimation method has high estimation accuracy, strong robustness, fast convergence speed, with the maximum SOC estimation error is less than 1%. INDEX TERMS Adaptive weighting factors, cubature particle filter, lithium battery, state of charge.

The complicated coupling of component design together with energy management has brought a significant challenge to the design, optimization, and control of plug-in hybrid electric buses (PHEBs). This paper proposes an integrated optimization methodology to ensure the optimum performance of a PHEB with a view toward designing and applications. First, a novel co-optimization method is proposed for redesigning the driveline parameters offline, which combines a nondominated sorting genetic algorithm-II (NSGA-II) with dynamic programming to eliminate the impact of the coupling between the component design and energy management. Within the new method, the driveline parameters are optimally designed based on a global optimal energy management strategy, and fuel consumption and acceleration time can be respectively reduced by 4.71% and 4.59%. Second, a model-free adaptive control (MFAC) method is employed to realize the online optimal control of energy management on the basis of Pontryagin's minimum principle (PMP). Particularly, an MFAC controller is used to track the predesigned linear state-of-charge (SOC), and its control variable is regarded as the co-state of the PMP. The main finding is that the co-state generated by the MFAC controller gradually converges on the optimal one derived according to the prior known driving cycles. This implies that the MFAC controller can realize a real-time application of the PMP strategy without acquiring the optimal co-state by offline calculation. Finally, the verification results demonstrated that the proposed MFAC-based method is applicable to both the typical and unknown stochastic driving cycles, meanwhile, and can further improve fuel economy compared to a conventional proportional-integral-differential (PID) controller.Processes 2019, 7, 477 2 of 23 on a single-objective (or multiobjective) optimization problem of driveline matching, component sizing, topology design, or the EMS [8][9][10][11]. For the predefined single-shaft coaxial parallel plug-in hybrid electric bus (PHEB) in our research, component sizing and the energy management control are extremely significant to fuel economy. To guarantee the optimality of dynamic performance and fuel economy, the driveline design and the EMS should be simultaneously considered, as they are strongly coupled [12]. Previous work has disposed of the combined optimization problem in a bilevel manner, where the outer loop is for the former and the inner loop is for the latter [8,13]. Many optimization algorithms have also been extensively applied to solve this problem, such as a genetic algorithm (GA) [4,14], particle swarm optimization (PSO) [11,15], and simulated annealing (SA) [9,16]. Most of them have been utilized to optimally design the component parameters for an outer loop, while a rule-based EMS is nested in an inner loop. However, the optimization results were suboptimal and influenced by the established rules due to the coupling relationship between the component design and EMS [3,7]. To overcome this drawback, another categ...

The state of charge (SOC) of lithium batteries is an important parameter of battery management systems. We aim at the problem that the noise variance is fixed during the estimation of the battery state by the unscented Kalman filter (UKF), which leads to low estimation accuracy. Lithium battery SOC estimation based on the UKF and whale optimization algorithm (WOA) is proposed. The first WOA is used to identify the parameters of the battery model. WOA–UKF is used to estimate the SOC of the battery, in which the observed noise variance and process noise variance of the UKF are updated through the second WOA, thereby the estimation accuracy is improved. The experimental results verify the effectiveness of the improved method.

Aiming at improving the tracking stability performance for intelligent electric vehicles, a novel stability coordinated control strategy based on preview characteristics is proposed in this paper. Firstly, the traditional stability control target is introduced with the two degrees of freedom model, which is realized by the sliding mode control strategy. Secondly, an auxiliary control target further amending the former one with the innovation formulation of the preview characteristics is established. At last, a multiple purpose Vague set leverages the contribution of the traditional target and the auxiliary preview target in various vehicle states. The proposed coordinated control strategy is analyzed on the MATLAB/CarSim simulation platform and verified on an intelligent electric vehicle established with A&D5435 rapid prototyping experiment platform. Simulation and experimental results indicate that the proposed control strategy based on preview characteristics can effectively improve the tracking stability performance of intelligent electric vehicles. In the double lane change simulation, the peak value of sideslip angle, yaw rate, and lateral acceleration of the vehicle is reduced by 13.2%, 11.4%, and 8.9% compared with traditional control strategy. The average deviations between the experimental and simulation results of yaw rate, lateral acceleration, and steering wheel angle are less than 10% at different speeds, which demonstrates the consistency between the experimental and the simulation results.