耗电量API
Android系统中很早就有耗电量的API,只不过一直都是隐藏的，Android系统的设置-电池功能就是调用的这个API，该API的核心部分是调用了com.android.internal.os.BatteryStatsHelper类，利用PowerProfile类，读取power_profile.xml文件，我们一起来看看具体如何计算耗电量，首先从最新版本6.0开始看
6.0的API
源码
BatteryStatsHelper
其中计算耗电量的方法为490行的processAppUsage,下来一步一步来解释该方法。
App耗电量的计算探究
private void processAppUsage(SparseArray<UserHandle> asUsers) {方法的参数是一个SparseArray数组，存储的对象是UserHandle,官方文档给出的解释是，代表一个用户，可以理解为这个类里面存储了用户的相关信息.
final boolean forAllUsers = (asUsers.get(UserHandle.USER_ALL) != null);然后判断该次计算是否针对所有用户，通过UserHandle的USER_ALL值来判断，该值为-1,源码的地址在https://github.com/DoctorQ/platform_frameworks_base/blob/android-6.0.0_r1/core/java/android/os/UserHandle.java.
mStatsPeriod = mTypeBatteryRealtime;然后给公共变量int类型的mStatsPeriod赋值,这个值mTypeBatteryRealtime的计算过程又在320行的refreshStats方法中:
mTypeBatteryRealtime = mStats.computeBatteryRealtime(rawRealtimeUs, mStatsType);这里面用到了BatteryStats(mStats)类中的computeBatteryRealtime方法,该方法计算出此次统计电量的时间间隔。好，歪楼了，回到BatteryStatsHelper中。
BatterySipper osSipper = null;
final SparseArray<? extends Uid> uidStats = mStats.getUidStats();
final int NU = uidStats.size();首先创建一个BatterySipper对象osSipper,该对象里面可以存储一些后续我们要计算的值，然后通过BatteryStats类对象mStats来得到一个包含Uid的对象的SparseArray组数,然后计算了一下这个数组的大小，保存在变量NU中。
for (int iu = 0; iu < NU; iu++) {
final Uid u = uidStats.valueAt(iu);
final BatterySipper app = new BatterySipper(BatterySipper.DrainType.APP, u, 0);然后for循环计算每个Uid代表的App的耗电量，因为BatterySipper可计算的类型有三种:应用, 系统服务, 硬件类型,所以这个地方传入的是DrainType.APP,还有其他可选类型如下:
public enum DrainType {
IDLE,
CELL,
PHONE,
WIFI,
BLUETOOTH,
FLASHLIGHT,
SCREEN,
APP,
USER,
UNACCOUNTED,
OVERCOUNTED,
CAMERA
}列举了目前可计算耗电量的模块。
mCpuPowerCalculator.calculateApp(app, u, mRawRealtime, mRawUptime, mStatsType);
mWakelockPowerCalculator.calculateApp(app, u, mRawRealtime, mRawUptime, mStatsType);
mMobileRadioPowerCalculator.calculateApp(app, u, mRawRealtime, mRawUptime, mStatsType);
mWifiPowerCalculator.calculateApp(app, u, mRawRealtime, mRawUptime, mStatsType);
mBluetoothPowerCalculator.calculateApp(app, u, mRawRealtime, mRawUptime, mStatsType);
mSensorPowerCalculator.calculateApp(app, u, mRawRealtime, mRawUptime, mStatsType);
mCameraPowerCalculator.calculateApp(app, u, mRawRealtime, mRawUptime, mStatsType);
mFlashlightPowerCalculator.calculateApp(app, u, mRawRealtime, mRawUptime, mStatsType);其中mStatsType的值为BatteryStats.STATS_SINCE_CHARGED,代表了我们的计算规则是从上次充满电后数据，还有一种规则是STATS_SINCE_UNPLUGGED是拔掉USB线后的数据。而mRawRealtime是当前时间，mRawUptime是运行时间。6.0的对各个模块的消耗都交给了单独的类去计算，这些类都继承于PowerCalculator抽象类：
蓝牙耗电:BluetoothPowerCalculator.java
摄像头耗电:CameraPowerCalculator.java
Cpu耗电:CpuPowerCalculator.java
手电筒耗电:FlashlightPowerCalculator.java
无线电耗电:MobileRadioPowerCalculator.java
传感器耗电:SensorPowerCalculator.java
Wakelock耗电:WakelockPowerCalculator.java
Wifi耗电:WifiPowerCalculator.java这一部分我一会单独拿出来挨个解释，现在我们还是回到BatteryStatsHelper继续往下走
final double totalPower = app.sumPower();BatterySipper#sumPower方法是统计总耗电量,方法详情如下，其中usagePowerMah这个值有点特殊，其他的上面都讲过.
/**
* Sum all the powers and store the value into `value`.
* @return the sum of all the power in this BatterySipper.
*/
public double sumPower() {
return totalPowerMah = usagePowerMah + wifiPowerMah + gpsPowerMah + cpuPowerMah +
sensorPowerMah + mobileRadioPowerMah + wakeLockPowerMah + cameraPowerMah +
flashlightPowerMah;
}然后根据是否是DEBUG版本打印信息，这个没啥可说的，然后会把刚才计算的电量值添加到列表中:
// Add the app to the list if it is consuming power.
if (totalPower != 0 || u.getUid() == 0) {
//
// Add the app to the app list, WiFi, Bluetooth, etc, or into "Other Users" list.
//
final int uid = app.getUid();
final int userId = UserHandle.getUserId(uid);
if (uid == Process.WIFI_UID) {
mWifiSippers.add(app);
} else if (uid == Process.BLUETOOTH_UID) {
mBluetoothSippers.add(app);
} else if (!forAllUsers && asUsers.get(userId) == null
&& UserHandle.getAppId(uid) >= Process.FIRST_APPLICATION_UID) {
// We are told to just report this user's apps as one large entry.
List<BatterySipper> list = mUserSippers.get(userId);
if (list == null) {
list = new ArrayList<>();
mUserSippers.put(userId, list);
}
list.add(app);
} else {
mUsageList.add(app);
}
if (uid == 0) {
osSipper = app;
}
}首先判断totalPower的值和当前uid号是否符合规则，规则为总耗电量不为0或者用户id为0.当uid表明为WIFI或者蓝牙时，添加到下面对应的列表中，一般情况下正常的应用我们直接保存到下面的mUsageList中就行就行，但是也有一些例外:
/**
* List of apps using power.
*/
private final List<BatterySipper> mUsageList = new ArrayList<>();
/**
* List of apps using wifi power.
*/
private final List<BatterySipper> mWifiSippers = new ArrayList<>();
/**
* List of apps using bluetooth power.
*/
private final List<BatterySipper> mBluetoothSippers = new ArrayList<>();如果我们的系统是单用户系统，且当前的userId号不在我们的统计范围内，且其进程id号是大于Process.FIRST_APPLICATION_UID(10000,系统分配给普通应用的其实id号),我们就要将其存放到mUserSippers数组中，定义如下:
private final SparseArray<List<BatterySipper>> mUserSippers = new SparseArray<>();最后判断uid为0的话，代表是Android操作系统的耗电量，赋值给osSipper(494行定义)就可以了,这样一个app的计算就完成了，遍历部分就不说了,保存这个osSipper是为了最后一步计算:
if (osSipper != null) {
// The device has probably been awake for longer than the screen on
// time and application wake lock time would account for. Assign
// this remainder to the OS, if possible.
mWakelockPowerCalculator.calculateRemaining(osSipper, mStats, mRawRealtime,
mRawUptime, mStatsType);
osSipper.sumPower();
}主流程我们已经介绍完了，下面来看各个子模块耗电量的计算
Cpu耗电量
Cpu的计算要用到PowerProfile类,该类主要是解析power_profile.xml:
<device name="Android">
<!-- Most values are the incremental current used by a feature,
in mA (measured at nominal voltage).
The default values are deliberately incorrect dummy values.
OEM's must measure and provide actual values before
shipping a device.
Example real-world values are given in comments, but they
are totally dependent on the platform and can vary
significantly, so should be measured on the shipping platform
with a power meter. -->
<item name="none">0</item>
<item name="screen.on">0.1</item> <!-- ~200mA -->
<item name="screen.full">0.1</item> <!-- ~300mA -->
<item name="bluetooth.active">0.1</item> <!-- Bluetooth data transfer, ~10mA -->
<item name="bluetooth.on">0.1</item> <!-- Bluetooth on & connectable, but not connected, ~0.1mA -->
<item name="wifi.on">0.1</item> <!-- ~3mA -->
<item name="wifi.active">0.1</item> <!-- WIFI data transfer, ~200mA -->
<item name="wifi.scan">0.1</item> <!-- WIFI network scanning, ~100mA -->
<item name="dsp.audio">0.1</item> <!-- ~10mA -->
<item name="dsp.video">0.1</item> <!-- ~50mA -->
<item name="camera.flashlight">0.1</item> <!-- Avg. power for camera flash, ~160mA -->
<item name="camera.avg">0.1</item> <!-- Avg. power use of camera in standard usecases, ~550mA -->
<item name="radio.active">0.1</item> <!-- ~200mA -->
<item name="radio.scanning">0.1</item> <!-- cellular radio scanning for signal, ~10mA -->
<item name="gps.on">0.1</item> <!-- ~50mA -->
<!-- Current consumed by the radio at different signal strengths, when paging -->
<array name="radio.on"> <!-- Strength 0 to BINS-1 -->
<value>0.2</value> <!-- ~2mA -->
<value>0.1</value> <!-- ~1mA -->
</array>
<!-- Different CPU speeds as reported in
/sys/devices/system/cpu/cpu0/cpufreq/stats/time_in_state -->
<array name="cpu.speeds">
<value>400000</value> <!-- 400 MHz CPU speed -->
</array>
<!-- Current when CPU is idle -->
<item name="cpu.idle">0.1</item>
<!-- Current at each CPU speed, as per 'cpu.speeds' -->
<array name="cpu.active">
<value>0.1</value> <!-- ~100mA -->
</array>
<!-- This is the battery capacity in mAh (measured at nominal voltage) -->
<item name="battery.capacity">1000</item>
<array name="wifi.batchedscan"> <!-- mA -->
<value>.0002</value> <!-- 1-8/hr -->
<value>.002</value> <!-- 9-64/hr -->
<value>.02</value> <!-- 65-512/hr -->
<value>.2</value> <!-- 513-4,096/hr -->
<value>2</value> <!-- 4097-/hr -->
</array>
</device>这个里面存储了Cpu(cpu.speeds)的主频等级，以及每个主频每秒消耗的毫安(cpu.active)，好，现在回到CpuPowerCalculator中，先来看构造方法
public CpuPowerCalculator(PowerProfile profile) {
final int speedSteps = profile.getNumSpeedSteps();
mPowerCpuNormal = new double[speedSteps];
mSpeedStepTimes = new long[speedSteps];
for (int p = 0; p < speedSteps; p++) {
mPowerCpuNormal[p] = profile.getAveragePower(PowerProfile.POWER_CPU_ACTIVE, p);
}
}第一步获得Cpu有几个主频等级，因为不同等级消耗的电量不一样，所以要区别对待，根据主频的个数，然后初始化mPowerCpuNormal和mSpeedStepTimes,前者用来保存不同等级的耗电速度，后者用来保存在不同等级上耗时，然后给mPowerCpuNormal的每个元素附上值。构造方法就完成了其所有的工作，现在来计算方法calculateApp,
final int speedSteps = mSpeedStepTimes.length;
long totalTimeAtSpeeds = 0;
for (int step = 0; step < speedSteps; step++) {
mSpeedStepTimes[step] = u.getTimeAtCpuSpeed(step, statsType);
totalTimeAtSpeeds += mSpeedStepTimes[step];
}
totalTimeAtSpeeds = Math.max(totalTimeAtSpeeds, 1);
首先得到Cpu主频等级个数，然后BatteryStats.Uid得到不同主频上执行时间，计算Cpu总耗时保存在totalTimeAtSpeeds中，
app.cpuTimeMs = (u.getUserCpuTimeUs(statsType) + u.getSystemCpuTimeUs(statsType)) / 1000;Cpu的执行时间分很多部分，但是我们关注User和Kernal部分,也就是上面的UserCpuTime和SystemCpuTime。
double cpuPowerMaMs = 0;
for (int step = 0; step < speedSteps; step++) {
final double ratio = (double) mSpeedStepTimes[step] / totalTimeAtSpeeds;
final double cpuSpeedStepPower = ratio * app.cpuTimeMs * mPowerCpuNormal[step];
if (DEBUG && ratio != 0) {
Log.d(TAG, "UID " + u.getUid() + ": CPU step #"
+ step + " ratio=" + BatteryStatsHelper.makemAh(ratio) + " power="
+ BatteryStatsHelper.makemAh(cpuSpeedStepPower / (60 * 60 * 1000)));
}
cpuPowerMaMs += cpuSpeedStepPower;
}上面的代码就是将不同主频的消耗累加到一起，但是其中值得注意的是，他并不是用各个主频的消耗时间*主频单位时间内消耗的电量，而是用一个radio变量来计算得到各个主频段执行时间占总时间的百分比，然后用cpuTimeMs来换算成各个主频的Cpu实际消耗时间，这比5.0的API多了这么一步，我估计是发现了计算的不严谨性，这也是Android迟迟不放出统计电量方式的原因，其实google自己对这块也没有把握，所以才会造成不同API计算方式的差异。好，计算完我们的总消耗后，是不是就算完事了？如果你只需要得到一个App的耗电总量，上面的讲解已经足够了，但是6.0的API计算了每个App的不同进程的耗电量，这个我们就只当看看就行，暂时没什么实际意义。
// Keep track of the package with highest drain.
double highestDrain = 0;
app.cpuFgTimeMs = 0;
final ArrayMap<String, ? extends BatteryStats.Uid.Proc> processStats = u.getProcessStats();
final int processStatsCount = processStats.size();
for (int i = 0; i < processStatsCount; i++) {
final BatteryStats.Uid.Proc ps = processStats.valueAt(i);
final String processName = processStats.keyAt(i);
app.cpuFgTimeMs += ps.getForegroundTime(statsType);
final long costValue = ps.getUserTime(statsType) + ps.getSystemTime(statsType)
+ ps.getForegroundTime(statsType);
// Each App can have multiple packages and with multiple running processes.
// Keep track of the package who's process has the highest drain.
if (app.packageWithHighestDrain == null ||
app.packageWithHighestDrain.startsWith("*")) {
highestDrain = costValue;
app.packageWithHighestDrain = processName;
} else if (highestDrain < costValue && !processName.startsWith("*")) {
highestDrain = costValue;
app.packageWithHighestDrain = processName;
}
}
// Ensure that the CPU times make sense.
if (app.cpuFgTimeMs > app.cpuTimeMs) {
if (DEBUG && app.cpuFgTimeMs > app.cpuTimeMs + 10000) {
Log.d(TAG, "WARNING! Cputime is more than 10 seconds behind Foreground time");
}
// Statistics may not have been gathered yet.
app.cpuTimeMs = app.cpuFgTimeMs;
}上面统计同一App下不同的进程的耗电量，得到消耗最大的进程名，保存到BatterySipper对象中,然后得出App的Cpu的foreground消耗时间,将foreground时间与之前计算得到的cpuTimeMs进行比较，如果foreground时间比cpuTimeMs还要大，那么就将cpuTimeMs的时间改变为foreground的值，但是这个值的变化对之前耗电总量的计算没有丝毫影响。
// Convert the CPU power to mAh
app.cpuPowerMah = cpuPowerMaMs / (60 * 60 * 1000);最后的最后，将耗电量用mAh单位来表示，所以在毫秒的基础上除以60*60*1000。
总结:Cpu耗电量的计算是要区分不同主频的，频率不同，单位时间内消耗的电量是有区分的，这一点要明白。还有一点就是不同主频上的执行时间不是通过BatteryStats.Uid#getTimeAtCpuSpeed方法得到的，二十是通过百分比和BatteryStats.Uid#getUserCpuTimeUs和getSystemCpuTimeUs计算得到cpuTimeMs乘积得到的。最后一点就是，cpuTimeMs时间是会在计算完毕后进行比较，比较的对象是CPU的foreground时间。
WakeLock耗电量的计算
从构造方法开始，
public WakelockPowerCalculator(PowerProfile profile) {
mPowerWakelock = profile.getAveragePower(PowerProfile.POWER_CPU_AWAKE);
}首先得到power_profile.xml中cpu.awake表示的值，保存在mPowerWakelock变量中。构造方法只做了这么点事，下面进入calculateApp方法。
@Override
public void calculateApp(BatterySipper app, BatteryStats.Uid u, long rawRealtimeUs,
long rawUptimeUs, int statsType) {
long wakeLockTimeUs = 0;
final ArrayMap<String, ? extends BatteryStats.Uid.Wakelock> wakelockStats =
u.getWakelockStats();
final int wakelockStatsCount = wakelockStats.size();
for (int i = 0; i < wakelockStatsCount; i++) {
final BatteryStats.Uid.Wakelock wakelock = wakelockStats.valueAt(i);
// Only care about partial wake locks since full wake locks
// are canceled when the user turns the screen off.
BatteryStats.Timer timer = wakelock.getWakeTime(BatteryStats.WAKE_TYPE_PARTIAL);
if (timer != null) {
wakeLockTimeUs += timer.getTotalTimeLocked(rawRealtimeUs, statsType);
}
}
app.wakeLockTimeMs = wakeLockTimeUs / 1000; // convert to millis
mTotalAppWakelockTimeMs += app.wakeLockTimeMs;
// Add cost of holding a wake lock.
app.wakeLockPowerMah = (app.wakeLockTimeMs * mPowerWakelock) / (1000*60*60);
if (DEBUG && app.wakeLockPowerMah != 0) {
Log.d(TAG, "UID " + u.getUid() + ": wake " + app.wakeLockTimeMs
+ " power=" + BatteryStatsHelper.makemAh(app.wakeLockPowerMah));
}
}首先获得Wakelock的数量，然后逐个遍历得到每个Wakelock对象，得到该对象后，得到BatteryStats.WAKE_TYPE_PARTIAL的唤醒时间，然后累加，其实wakelock有4种，为什么只取partial的时间,具体代码google也没解释的很清楚,只是用一句注释打发了我们。得到总时间后，就可以与构造方法中的单位时间waklock消耗电量相乘得到Wakelock消耗的总电量。
Wifi耗电量的计算
首先来看构造方法，来了解一下WIFI的耗电量计算用到了power_profile.xml中的哪些属性:
public WifiPowerCalculator(PowerProfile profile) {
mIdleCurrentMa = profile.getAveragePower(PowerProfile.POWER_WIFI_CONTROLLER_IDLE);
mTxCurrentMa = profile.getAveragePower(PowerProfile.POWER_WIFI_CONTROLLER_TX);
mRxCurrentMa = profile.getAveragePower(PowerProfile.POWER_WIFI_CONTROLLER_RX);
}我们去PowerProfile.java找到上面三个常量代表的属性:
public static final String POWER_WIFI_CONTROLLER_IDLE = "wifi.controller.idle";
public static final String POWER_WIFI_CONTROLLER_RX = "wifi.controller.rx";
public static final String POWER_WIFI_CONTROLLER_TX = "wifi.controller.tx";知道对应的xml的属性后我们直接看calculateApp方法:
@Override
public void calculateApp(BatterySipper app, BatteryStats.Uid u, long rawRealtimeUs,
long rawUptimeUs, int statsType) {
final long idleTime = u.getWifiControllerActivity(BatteryStats.CONTROLLER_IDLE_TIME,
statsType);
final long txTime = u.getWifiControllerActivity(BatteryStats.CONTROLLER_TX_TIME, statsType);
final long rxTime = u.getWifiControllerActivity(BatteryStats.CONTROLLER_RX_TIME, statsType);
app.wifiRunningTimeMs = idleTime + rxTime + txTime;
app.wifiPowerMah =
((idleTime * mIdleCurrentMa) + (txTime * mTxCurrentMa) + (rxTime * mRxCurrentMa))
/ (1000*60*60);
mTotalAppPowerDrain += app.wifiPowerMah;
app.wifiRxPackets = u.getNetworkActivityPackets(BatteryStats.NETWORK_WIFI_RX_DATA,
statsType);
app.wifiTxPackets = u.getNetworkActivityPackets(BatteryStats.NETWORK_WIFI_TX_DATA,
statsType);
app.wifiRxBytes = u.getNetworkActivityBytes(BatteryStats.NETWORK_WIFI_RX_DATA,
statsType);
app.wifiTxBytes = u.getNetworkActivityBytes(BatteryStats.NETWORK_WIFI_TX_DATA,
statsType);
if (DEBUG && app.wifiPowerMah != 0) {
Log.d(TAG, "UID " + u.getUid() + ": idle=" + idleTime + "ms rx=" + rxTime + "ms tx=" +
txTime + "ms power=" + BatteryStatsHelper.makemAh(app.wifiPowerMah));
}
}这里的计算方式也是差不多，先根据Uid得到时间，然后乘以构造方法里对应的wifi类型单位时间内消耗电量值，没什么难点，就不一一分析，需要注意的是，这里面还计算了wifi传输的数据包的数量和字节数。
蓝牙耗电量的计算
蓝牙关注的power_profile.xml中的属性如下:
public static final String POWER_BLUETOOTH_CONTROLLER_IDLE = "bluetooth.controller.idle";
public static final String POWER_BLUETOOTH_CONTROLLER_RX = "bluetooth.controller.rx";
public static final String POWER_BLUETOOTH_CONTROLLER_TX = "bluetooth.controller.tx";但是还没有单独为App计算耗电量的，所以这个地方是空的。
@Override
public void calculateApp(BatterySipper app, BatteryStats.Uid u, long rawRealtimeUs,
long rawUptimeUs, int statsType) {
// No per-app distribution yet.
}摄像头耗电量的计算
摄像头的耗电量关注的是power_profile.xml中camera.avg属性代表的值，保存到mCameraPowerOnAvg,
public static final String POWER_CAMERA = "camera.avg";计算方式如下:
@Override
public void calculateApp(BatterySipper app, BatteryStats.Uid u, long rawRealtimeUs,
long rawUptimeUs, int statsType) {
// Calculate camera power usage. Right now, this is a (very) rough estimate based on the
// average power usage for a typical camera application.
final BatteryStats.Timer timer = u.getCameraTurnedOnTimer();
if (timer != null) {
final long totalTime = timer.getTotalTimeLocked(rawRealtimeUs, statsType) / 1000;
app.cameraTimeMs = totalTime;
app.cameraPowerMah = (totalTime * mCameraPowerOnAvg) / (1000*60*60);
} else {
app.cameraTimeMs = 0;
app.cameraPowerMah = 0;
}
}先计算摄像头打开的时间totalTime，然后根据这个值乘以mCameraPowerOnAvg得到摄像头的耗电量。
手电筒耗电量的计算
FlashlightPowerCalculator.java
public static final String POWER_FLASHLIGHT = "camera.flashlight";跟摄像头类似，也是先得到时间，然后乘积，不想说了，没意思。
无线电耗电量的计算
MobileRadioPowerCalculator.java
关注的是power_profile.xml中如下三个属性:
/**
* Power consumption when screen is on, not including the backlight power.
*/
public static final String POWER_SCREEN_ON = "screen.on";
/**
* Power consumption when cell radio is on but not on a call.
*/
public static final String POWER_RADIO_ON = "radio.on";
/**
* Power consumption when cell radio is hunting for a signal.
*/
public static final String POWER_RADIO_SCANNING = "radio.scanning";当无限量连接上时，根据信号强度不同，耗电量的计算是有区别的，所以在构造方法，当无线电的状态为on时，是要特殊处理的，其他两个状态(active和scan)就正常取值就可以了。
/**
* Power consumption when screen is on, not including the backlight power.
*/
public static final String POWER_SCREEN_ON = "screen.on";
/**
* Power consumption when cell radio is on but not on a call.
*/
public static final String POWER_RADIO_ON = "radio.on";
/**
* Power consumption when cell radio is hunting for a signal.
*/
public static final String POWER_RADIO_SCANNING = "radio.scanning";计算的方式分两种，以无线电处于active状态的次数为区分，当active大于0，我们用处于active状态的时间来乘以它的单位耗时。另一种情况就要根据网络转化的数据包来计算耗电量了。
传感器耗电量的计算
只关注一个属性:
public static final String POWER_GPS_ON = "gps.on";- 1
计算方式如下:
@Override
public void calculateApp(BatterySipper app, BatteryStats.Uid u, long rawRealtimeUs,
long rawUptimeUs, int statsType) {
// Process Sensor usage
final SparseArray<? extends BatteryStats.Uid.Sensor> sensorStats = u.getSensorStats();
final int NSE = sensorStats.size();
for (int ise = 0; ise < NSE; ise++) {
final BatteryStats.Uid.Sensor sensor = sensorStats.valueAt(ise);
final int sensorHandle = sensorStats.keyAt(ise);
final BatteryStats.Timer timer = sensor.getSensorTime();
final long sensorTime = timer.getTotalTimeLocked(rawRealtimeUs, statsType) / 1000;
switch (sensorHandle) {
case BatteryStats.Uid.Sensor.GPS:
app.gpsTimeMs = sensorTime;
app.gpsPowerMah = (app.gpsTimeMs * mGpsPowerOn) / (1000*60*60);
break;
default:
final int sensorsCount = mSensors.size();
for (int i = 0; i < sensorsCount; i++) {
final Sensor s = mSensors.get(i);
if (s.getHandle() == sensorHandle) {
app.sensorPowerMah += (sensorTime * s.getPower()) / (1000*60*60);
break;
}
}
break;
}
}
}当传感器的类型为GPS时，我们计算每个传感器的时间然后乘以耗电量，和所有的耗电量计算都是一样，不同的是，当传感器不是GPS时，这个时候计算就根据SensorManager得到所有传感器类型，这个里面保存有不同传感器的单位耗电量，这样就能计算不同传感器的耗电量。
总结
至此我已经把App耗电量的计算讲完了(还有硬件)，前后花费3天时间，好痛苦(此处一万只草泥马)，不过好在自己也算对这个耗电量的理解有了一定的认识。google官方对耗电量的统计给出的解释都是不能代表真实数据，只能作为参考值，因为受power_profile.xml的干扰太大，如果手机厂商没有严格设置这个文件，那可想而知出来的值可能是不合理的。
提示
腾讯的GT团队前几天推出了耗电量的计算APK，原理是一样的，大家可以试用下GT