The decreasing of fossil fuel supply and increasing requirements for the clean air have forced many governments and automotive companies to pay more attention to the development of electric vehicles (EVs). The development of battery EVs (BEVs) and plug-in hybrid EVs (PHEVs) is popular due to their environmental-friendly and energy saving characteristics. Compared with conventional vehicles, PHEVs have more flexibility in the working points control for the engine, which makes it have better fuel consumption performance. The BEVs can realize zero emission as all of their power comes from the batteries and the electric power in the batteries may come from solar energy or wind energy, which is renewable energy. However, the battery used in the EVs becomes a technical bottleneck as it still has some problems, such as the long charging time or precise estimation of state of charge (SOC
]. The charging time for EVs is usually long, which is a serious problem for their wide application. Therefore, the charging process of the battery needs to be considered in order to improve the charging performance [6
]. Another challenge for the wide application of the EVs is from the grid. When the EVs are charged from the grids without regulations, the grids may not work very well [8
Currently, many battery charging strategies have been proposed with the development of EVs, such as constant trickle (CTC), constant current (CC), constant voltage (CV), and constant current constant voltage (CCCV) battery charge strategies [10
]. Among these charging strategies, the CCCV charging technique is always the most popular type. In the CCCV charging process, the battery will be charged at a CC until the battery voltage reaches its upper cutoff voltage. Then the battery will experience a CV charging process until the current reaches a predetermined small value. Researchers have proposed many approaches to improve the charging performance of the battery. Liu et al.
] proposed an Ant-Colony-System (ACS) based algorithm to get the optimal rapid charging pattern, but in the proposed multistage CC charging algorithm, the charging duration for each stage is not optimized. To increase the charging speed and maintain the charging process in a safe-charge area, Hsieh et al.
] used a fuzzy-controlled active SOC
controller to replace the general CV charging mode. For high power charging, Surmann [16
] presented a genetic optimization method of a fuzzy system to charging the high power NiCd batteries. Ullah et al.
] designed a superfast battery charger by national’s proprietary neural network based NeuFuz’ technology, to charge a NiCd battery pack. Chen et al.
] viewed the Li-ion battery as a grey system and used the grey prediction technique to develop a grey-predicted Li-ion battery charge system. After considering the conflict objectives of charging time and charging loss, Hu et al.
] proposed a dual-objective optimal charging strategy for two types of Li-ion batteries and analyzed the influences of the charging voltage threshold, temperature, and health status on the charging results. Most of the work discussed above aims to accelerate the charging speed. However, for the EVs people may have different charging requirements for their vehicles in different conditions. With this in mind, this paper proposed a databased dynamic programing (DP) method which can efficiently and effectively get the suboptimal and implementable charging strategies under different charging requirements.
The paper is organized as follows: in Section 2
, the lumped parameter battery model is illustrated followed by the detailed explaining of the main parameters of the model; the dynamic optimization problem, DP simulation results and the abstracted rules are introduced in Section 3
; the working principle of the databases, construction of the databases and simulation results of database-based DP method are given in Section 4
; finally, conclusions are presented in Section 5